Synthetic Media and Deepfakes: Safeguarding the 2025 Election Cycle

Advances in artificial intelligence have enabled the creation of synthetic media – content generated or manipulated by AI – on an unprecedented scale. As democratic nations head into the 2025 election cycle, officials and experts are sounding alarms about AI-driven disinformation. In a recent survey, 85% of Americans voiced concern about “misleading video and audio deepfakes” affecting elections brennancenter.org. News headlines warn that AI-generated “deepfakes” could wreak havoc on campaigns and voter trust brennancenter.org, underscoring the urgency to protect electoral integrity. This report examines what synthetic media and deepfakes are, how they threaten democracy, and what can be done – from technology solutions to policies – to safeguard elections in 2025 and beyond.

What Are Synthetic Media and Deepfakes?

Synthetic media is a broad term for digital content (images, video, audio, text) that is artificially produced or altered by automated means, especially AI algorithms en.wikipedia.org. Today’s generative AI systems can create realistic human-like output in every medium – from lifelike photos of people who never existed, to cloned voices and AI-written articles. Deepfakes are a particular subset of synthetic media: highly realistic fake images, videos or audio crafted with AI (hence “deep” learning + “fake”) to impersonate real people encyclopedia.kaspersky.com. In practice, a deepfake might be a video where a politician’s face is convincingly swapped onto someone else’s body, or an audio clip mimicking a candidate’s voice saying words they never actually said.

How are deepfakes created? Most are generated through advanced deep learning techniques. A common approach uses generative adversarial networks (GANs) – two neural networks that train against each other icct.nl. One network (the generator) fabricates fake media (e.g. an image of a person’s face) and the other (the discriminator) tries to detect if it’s fake. Through thousands of iterations, the generator learns to produce increasingly realistic output until the discriminator can no longer tell the difference icct.nl. Originally, creating a seamless deepfake required extensive training data and powerful hardware – for instance, an experiment to deepfake actor Tom Cruise took two months of training on high-end GPUs icct.nl. However, tools have rapidly evolved. Sophisticated deepfake software is now widely accessible and faster, sometimes even operating in real-time (for example, altering a live video feed or voice call on the fly) encyclopedia.kaspersky.com. Besides GANs, other AI architectures play a role: transformer models can generate deepfake text or assist in audio voice cloning encyclopedia.kaspersky.com. In sum, recent AI breakthroughs have made it easy and cheap for almost anyone to create deceptive audio-visual content – dramatically lowering the barrier to misinformation operations.

It’s important to note that not all synthetic media is malicious. AI-generated content can be used for benign and creative purposes – personalized avatars, dubbing a speaker’s voice into other languages, satire and entertainment, etc. In fact, in global elections during 2024, roughly half of documented uses of AI in political content were non-deceptive (e.g. a candidate transparently using an AI voice due to losing their own voice, or journalists using an AI avatar to protect their identity) knightcolumbia.org knightcolumbia.org. But this report focuses on the malicious side of synthetic media – deepfakes intended to mislead, deceive, or manipulate voters and public opinion.

Risks to Democratic Processes

Synthetic media and deepfakes pose significant risks to democracy, particularly during elections when an informed electorate and trust in information are paramount. Key threats include:

Disinformation and Voter Manipulation: AI-generated fake videos, images or audio can be used to spread false information about candidates or issues, misleading voters. For example, a deepfake might depict a candidate making inflammatory statements they never actually made. Such fabricated content can inject toxic falsehoods into the public debate. Experts warn that deepfakes “pose a high risk” to voters by injecting false content into campaigns and eroding public trust aljazeera.com. A convincingly doctored video released shortly before Election Day – with no time for fact-checkers to debunk it – could even sway undecided voters or suppress turnout, potentially altering the outcome citizen.org. This threat is not just theoretical: as detailed later, a deepfake audio in 2024 impersonated the U.S. President urging supporters not to vote, apparently aiming to discourage turnout aljazeera.com aljazeera.com.
Erosion of Trust (“Liar’s Dividend”): Beyond any specific fake, the mere existence of deepfakes can undermine public trust in real information. Voters may start doubting authentic evidence, unsure if a viral video is real or an AI forgery. Even worse, corrupt actors can exploit this doubt: real scandals or truthful recordings can be dismissed as “just a deepfake,” allowing wrongdoers to escape accountability. Scholars have dubbed this the “liar’s dividend,” where increased awareness of deepfakes makes it easier for liars to claim that authentic footage is fake brennancenter.org. Heightened public awareness of AI’s power means a politician caught in an actual misdeed might more readily deceive the public by labeling damning audio or video as AI-generated hoax brennancenter.org. This dynamic threatens the fundamental trust on which democratic discourse relies. Election observers noted that in 2024 some candidates and their supporters preemptively cried “AI fake” to dismiss uncomfortable stories brennancenter.org brennancenter.org. In the long run, if citizens feel “you can’t trust anything you see or hear”, it erodes the shared reality needed for free and fair elections cetas.turing.ac.uk cetas.turing.ac.uk.
Amplifying Polarization and Conflict: Thus far, evidence suggests deepfake propaganda often reinforces people’s pre-existing biases rather than persuading across lines cetas.turing.ac.uk. Malicious AI content is frequently embraced and spread by those who already hold extreme views, which amplifies echo chambers. During the 2024 U.S. presidential race, researchers found AI-generated falsehoods mostly served to intensify partisan narratives and inflame debates, rather than convert new believers cetas.turing.ac.uk. For instance, fake videos targeting President Biden or Vice President Harris attracted millions of views online and were largely circulated by users already hostile to them cetas.turing.ac.uk cetas.turing.ac.uk. By consolidating ideological camps with dramatic fake “evidence” of the other side’s perfidy, deepfakes can drive communities further apart, toxifying the campaign environment. Moreover, the confusion and distrust deepfakes sow create fertile ground for conspiracy theories to thrive cetas.turing.ac.uk, since citizens can more easily reject any inconvenient reality as an AI fabrication.
Undermining Election Administration: The risk goes beyond misleading voters about candidates – deepfakes could also disrupt the electoral process itself. Officials have envisioned scenarios where AI voice clones or fake messages purport to be from election authorities, telling poll workers to close polling stations early or giving voters false instructions (e.g. “the election has been postponed”) aljazeera.com. A sophisticated adversary might simulate a directive from an election commission or a trusted local official’s voice to sabotage voting operations. Such tactics could suppress votes or spark chaos on Election Day. The U.S. Brennan Center notes that manipulated media could be used to deceive not only the public but also poll workers and election officials, requiring new training and protocols in response aljazeera.com.
Harassment and Character Assassination: Deepfakes also provide a potent weapon for personal attacks on candidates, activists, or journalists. An especially pernicious category is non-consensual synthetic pornography – taking a person’s face and grafting it into explicit sexual content. This tactic has already been used to harass female journalists and politicians around the world. The most extreme form of deepfake harassment is fake intimate imagery used to humiliate or blackmail individuals weforum.org. In an election context, operatives could release a bogus compromising video of a candidate (for example, a deepfake sex tape or a fake recording of them engaging in illegal behavior) shortly before a vote. Even if quickly debunked, the damage to the candidate’s reputation may be done. Women and minorities are disproportionately targeted by these “synthetic smear” campaigns, which can discourage diverse candidates from running for office policyoptions.irpp.org policyoptions.irpp.org. In summary, deepfakes add new fuel to old dirty tricks – from fake scandals to forged quotes – supercharging character assassination attempts in elections.

Finally, it must be noted that so far we have not witnessed a deepfake-induced electoral catastrophe. Empirical analyses of 2024 elections worldwide found little evidence that AI-generated disinformation changed any election result cetas.turing.ac.uk weforum.org. Traditional misinformation (cheaply edited “cheapfakes,” rumors, partisan spin) remained a far larger factor in spreading falsehoods than high-tech deepfakes knightcolumbia.org knightcolumbia.org. However, experts caution that the absence of a disaster so far is no reason for complacency cetas.turing.ac.uk weforum.org. The technology is advancing rapidly, and hostile actors are learning. Even if deepfakes did not swing a major 2024 race, they did shape the discourse – for example, viral AI-generated lies about candidates became talking points in mainstream debates cetas.turing.ac.uk. Moreover, the perceived threat of deepfakes itself contributed to public anxiety and distrust around elections cetas.turing.ac.uk cetas.turing.ac.uk. The potential for a more damaging incident remains, especially as we approach 2025’s high-stakes elections. Democratic societies must therefore treat deepfakes as a serious security and integrity issue, addressing both the direct risk of fabricated media and the broader erosion of truth in the electoral sphere.

Recent Incidents: Deepfakes Disrupting Politics

Real-world cases from the past few years illustrate how synthetic media has already been weaponized in political contexts. Below we review several notable incidents and case studies of deepfakes and AI-generated misinformation affecting elections or public discourse:

Ukraine (March 2022) – “Surrender” Video: In the early days of Russia’s war on Ukraine, a video emerged appearing to show Ukrainian President Volodymyr Zelensky urging his troops to lay down arms and surrender. The video was a deepfake, with Zelensky’s image and voice synthetically altered icct.nl. Tell-tale flaws (blurry edges, mismatched neck tone) gave it away, and Ukrainian media quickly exposed the hoax. This incident – the first known use of a deepfake in an armed conflict – foreshadowed how AI propaganda could be used to undermine leaders during crises icct.nl. While the fake Zelensky video did not succeed in demoralizing Ukraine’s resistance, it demonstrated the intent and ability of malicious actors (in this case suspected Russian operatives) to use deepfakes for information warfare.
Slovakia (September 2023) – Election Disinformation: Just days before Slovakia’s parliamentary elections, deepfake audio recordings went viral purporting to feature Michal Šimečka, leader of the Progressive Slovakia party, confessing to election fraud and even proposing to double the price of beer brennancenter.org. Some versions had a faint disclaimer that they were AI-generated, but it appeared only at the end of the clip – likely a deliberate ploy to mislead listeners brennancenter.org. The timing was clearly strategic, coming right before voting. Šimečka’s pro-Western party narrowly lost to a pro-Kremlin rival, and some commentators speculated that the last-minute deepfake smear may have affected the result brennancenter.org. This case underscores how foreign or domestic actors can deploy deepfakes to sway a tight race, and how difficult it can be to counter false narratives in the final moments of a campaign.
Taiwan (January 2024) – Foreign Influence Operations: Ahead of Taiwan’s 2024 presidential election, observers documented a Chinese disinformation campaign using deepfakes to undermine candidate Lai Ching-te. Fake videos circulated online showing Lai (of the ruling pro-independence party) making statements he never made – for instance, falsely implying he supported his opponents’ platform policyoptions.irpp.org. In one case, AI-generated audio of Lai was released that appeared to feature him criticizing his own party policyoptions.irpp.org, attempting to fracture his support. These synthetic media attacks, traced to China, aimed to influence public opinion and sow confusion in Taiwan’s democracy policyoptions.irpp.org. Ultimately, Lai won the election and analysts assessed that the Chinese deepfake campaign did not significantly change the outcome policyoptions.irpp.org. However, it provided a textbook example of a hostile foreign power using AI propaganda against a democratic election policyoptions.irpp.org policyoptions.irpp.org. The concern remains that in a closer election elsewhere, such tactics could have greater impact.
United States (2024) – Deepfakes in the Campaign: The 2024 U.S. election cycle saw a surge of AI-generated political content that, while not derailing the election, raised alarm. In early 2024, voters in New Hampshire received a baffling robocall: a voice resembling President Joe Biden telling Democrats “to save your vote, don’t use it in this election.” The voice sounded authentic to some, but the message was obviously suspect – Biden would never urge supporters not to vote. In truth it was a deepfake voice clone of Biden, sent to thousands of voters in an apparent attempt at voter suppression aljazeera.com aljazeera.com. This incident, reaching an estimated 5,000 New Hampshire phone numbers, illustrated how cheaply and easily such dirty tricks can be pulled off – the consultant who created the Biden voice deepfake said it took only 20 minutes and about $1 of computing cost policyoptions.irpp.org policyoptions.irpp.org. Meanwhile, on social media, AI-generated imagery made its way into official campaign materials. Notably, Florida Governor Ron DeSantis’s team released an attack ad featuring doctored images of Donald Trump hugging Dr. Anthony Fauci – the implication being Trump was overly friendly with the former COVID advisor unpopular on the right. It turned out the images of Trump embracing Fauci were AI-generated fakes inserted into the video by the campaign brennancenter.org, leading to public criticism once discovered. In another case, an AI-made video of President Biden “addressing” the nation with slurred speech spread online, only to be debunked. Some fake videos of Biden and Vice President Harris amassed millions of views on social media cetas.turing.ac.uk, showing how quickly such content can proliferate. Even tech moguls got involved: Elon Musk infamously re-shared a crudely altered video of VP Harris (labeled “satire”) that depicted her spewing nonsense – blurring the line between meme humor and disinformation cetas.turing.ac.uk cetas.turing.ac.uk. While none of these deepfakes changed the trajectory of the election, they reinforced false narratives (for example, about Biden’s mental acuity or Trump’s loyalties) and further poisoned the information environment. U.S. officials also worry about deepfakes targeting election infrastructure – e.g. fake audio of election supervisors instructing staff to take improper actions aljazeera.com – although no major incident of that type was publicly confirmed in 2024.

These examples highlight the global scope of the threat. Deepfakes have been used by state actors in geopolitical conflicts, by provocateurs in domestic elections from Europe to Asia, and by campaigns and supporters in the United States. They have taken the form of fake speeches, images, phone calls, and videos – targeting both voters and election officials. The incidents so far also yield some lessons: many deepfakes have been detected and exposed relatively quickly (often by alert journalists or fact-checkers), and in several cases the backlash against using deepfakes (e.g. the DeSantis ad) generated negative press for the perpetrators. This suggests transparency and vigilance can blunt their harm. However, the trend is clear – such synthetic falsehoods are becoming more frequent and harder to immediately distinguish from reality. Each election brings new firsts (2024 saw the first AI “robocall” scams to influence voting, the first campaign use of deepfake ads, etc.), and the risk of a more damaging deepfake incident looms larger as we approach 2025.

Detecting and Countering Deepfakes: Tools and Technologies

A critical component of safeguarding elections is developing reliable detection and mitigation tools against deepfakes. Researchers, tech companies, and governments are racing to create technologies that can spot AI forgeries and authenticate real content. Here we overview the current landscape of deepfake detection and related countermeasures:

Automated Deepfake Detectors: A primary line of defense is AI that fights AI – algorithms trained to analyze media and identify telltale signs of manipulation. These detection systems look for subtle artifacts or inconsistencies left behind by generative models. Early deepfakes, for example, often had irregular eye blinking or imperfect lip-sync. Today’s detectors use deep neural networks to scrutinize things like lighting and shadows on faces, audio frequency patterns, or biological signals (e.g. pulse in video) that AI might fail to replicate. Tech firms have built internal tools – for instance, Microsoft released a “Video Authenticator” in 2020 that could flag fake videos by frame analysis. Platforms like Facebook and X (Twitter) have invested in detection research and deploy some filters to catch known fake media. Academic initiatives and competitions (such as the Facebook Deepfake Detection Challenge and IEEE conferences) have spurred progress, and startups like Sensity and Reality Defender offer commercial deepfake detection services for clients. However, this is very much an arms race: as detection improves, deepfake creators adapt to produce more seamless fakes that evade automated checks. Notably, a 2023 Meta report found that out of all the misinformation flagged during the 2024 election cycle, “less than 1%” was identified as AI-generated content weforum.org, suggesting either that deepfakes were relatively rare or that many slipped past detection unnoticed.
Watermarking and Content Provenance: Another strategy is to tag AI-generated content at its creation so that downstream users can easily recognize it as synthetic. The EU is heavily promoting this approach – the new EU AI Act explicitly mandates that any AI-generated or AI-manipulated content be clearly labeled or watermarked as such realitydefender.com. Companies would be required to embed an indicator (a digital watermark or metadata marker) when an image, video, or audio is produced by AI. In theory, browsers or social media sites could then automatically flag or filter content with these markers. Watermarking has promise, especially for discouraging casual misuse. Major AI model providers (like OpenAI, Google, and others) have discussed voluntary watermarking of the images or text their systems generate. Additionally, a coalition of media and tech organizations is developing provenance standards (e.g. the C2PA, Coalition for Content Provenance and Authenticity) to cryptographically record the origin and edit history of digital media. For example, a news photo or campaign ad could carry a secure certificate of authenticity, allowing anyone to verify who created it and that it hasn’t been tampered with cetas.turing.ac.uk. The U.S. government is embracing this; the White House directed federal agencies to devise guidelines for “authenticity by design,” embedding provenance metadata in all digital content they produce by 2025 cetas.turing.ac.uk. If broadly adopted, such measures would make it much harder for fake content to masquerade as real.
Limitations of Labels: While transparency tools are crucial, they are not foolproof. Watermarks can be removed or altered by determined adversaries. Indeed, researchers have already shown methods to strip out or obscure AI watermarks realitydefender.com, and of course a malicious actor building their own generative model can simply choose not to include any markers. Provenance metadata, too, only helps if it’s widely implemented and if consumers actually check it. A deepfake creator can also use a “provenance piggybacking” trick – taking an authentic photo or video and overlaying fake elements on it, so that the end product still carries the original file’s digital signature. These challenges mean that we cannot rely solely on content labels. As one AI security firm noted, watermark and provenance solutions work only when content creators cooperate in labeling their output – which won’t stop dedicated bad actors realitydefender.com. For this reason, inference-based detection (analyzing the content itself for signs of AI manipulation) remains essential realitydefender.com. The best defense will likely combine both approaches: robust automated detectors scanning for fakes, and authentication systems to verify legitimate media.
Real-Time Detection for Video/Audio Streams: An emerging need is tools that can catch deepfakes in live settings. Consider a hypothetical scenario of a fraudulent “live” video call with a candidate or an official – as happened in 2023 when criminals in Hong Kong deepfaked a company executive’s likeness on Zoom to authorize a $25 million fraudulent transfer weforum.org. In that case, multiple people on the call – including an imposter of the CFO – were all AI-generated. Detecting such real-time fakes is extremely challenging. Companies are working on solutions like plugins for videoconferencing that can alert users if an image or voice seems synthetically altered (for instance, by analyzing audio latency and spectral anomalies, or checking if an onscreen face exactly matches the movements of a real person’s face as captured by a camera). Some startups claim to offer real-time deepfake detection APIs that could integrate into streaming platforms or even authenticate speakers at live events. For now, though, real-time detection tends to lag behind the attackers, and emphasis is on preventive measures (such as using passwords or shared “code words” in phone calls to verify identities, as law enforcement recommends weforum.org).
Human Fact-Checking and Community Flags: Technology alone is not a silver bullet. A vigilant human layer remains crucial. News organizations, fact-checking groups, and platforms have set up special teams to monitor for viral deepfakes during election periods. These teams use OSINT (open source intelligence) techniques and forensic tools to analyze suspicious media – for example, checking the timestamps, looking for inconsistencies (like mismatched earrings on a politician in a video, or weird mouth movements), and quickly publishing debunkings. Crowdsourced efforts also help: on X/Twitter, the “Community Notes” feature has been used to flag posts containing AI-generated images or video with clarifying context. During recent elections, users often exposed deepfakes within hours of their appearance, posting side-by-side comparisons or pointing to flaws. This kind of collective vigilance, aided by digital literacy, is a powerful tool. Platforms are increasingly leaning on users and independent fact-checkers to identify dubious content, given the sheer scale of what automated filters must scan. The drawback is that a deepfake can go viral before it’s debunked. Nonetheless, improving response speed and broadening awareness (so more users can spot a fake themselves) will mitigate harm.

In summary, deepfake detection is an active and evolving field. Progress is being made – for instance, detectors today are far better than in 2018, and initiatives like the Content Authenticity Initiative aim to make verification standard. But challenges remain due to the cat-and-mouse dynamic with adversaries and the need for widespread adoption of tools. The coming years will likely see detection tech integrated more into social media platforms, news workflows, and even devices (imagine your smartphone warning you that an incoming video might be AI-generated). Crucially, detection and provenance tools must be coupled with public education so that when an alert or label does appear, users understand and act accordingly. This technology piece is only one pillar of a larger strategy required to counter synthetic media threats.

Policy Responses and Regulatory Frameworks

Policymakers around the world have awakened to the deepfake threat and begun crafting laws and regulations to address it. While the issue is novel, a patchwork of responses is emerging across major democracies. Below is an overview of legislative and regulatory efforts underway:

United States: In the U.S., there is currently no blanket federal law against political deepfakes, but momentum is building to fill that gap. Multiple bills have been introduced in Congress aiming to curb malicious deepfakes. For example, in early 2024 lawmakers proposed the No AI FRAUD Act in response to high-profile incidents (like AI-generated explicit images of celebrities) policyoptions.irpp.org. This bill seeks to establish a federal framework criminalizing certain harmful uses of AI, such as fraudulent political deepfakes and deceptive pornographic fakes policyoptions.irpp.org. Another legislative idea in discussion is mandating disclosure of AI-generated content in election ads (so that campaigns must include clear labels if an ad contains synthetic media). Meanwhile, the Federal Communications Commission (FCC) took a targeted step by banning the use of AI voice clones in robocalls intended to defraud or cause harm policyoptions.irpp.org. This was prompted by scams where imposters mimicked voices of real people. The agency’s move makes it illegal for telemarketers or political operatives to use synthetic voice messages to mislead recipients. Much deepfake regulation in the U.S. is happening at the state level. Since 2019, states including California, Texas, and others have passed laws addressing election deepfakes. California prohibits distributing materially deceptive deepfake videos of candidates within 60 days of an election (with exceptions for satire/parody) brennancenter.org. Texas made it a state jail felony to create or share deepfake videos intended to injure a candidate or influence voters brennancenter.org. As of mid-2025, at least fourteen U.S. states have enacted or are debating legislation to regulate deepfakes in election contexts citizen.org. Notably, these efforts have attracted bipartisan support – lawmakers of both parties agree that AI-manipulated election disinformation is a threat to democracy citizen.org citizen.org. State laws vary in approach: some impose criminal penalties for publishing harmful deepfakes about a candidate, while others focus on requiring warning labels on synthetic media used in campaign ads. Additionally, the advocacy group Public Citizen petitioned the Federal Election Commission to update its rules, urging the FEC to ban federal candidates from disseminating deceptive deepfakes in campaigns brennancenter.org. Though the FEC hasn’t yet issued new regulations, the issue is clearly on the agenda. U.S. policymakers must also balance free speech concerns – overly broad bans on manipulated media can collide with the First Amendment. For instance, satire and parody (protected political speech) often involve doctored images or videos; laws must be crafted to target only malicious deception. This is reflected in many state statutes that explicitly carve out exceptions for parody, satire, or journalistic uses brennancenter.org brennancenter.org. The general consensus, however, is that false AI-generated content that deliberately seeks to mislead voters or incite disruption has no legitimate value in a democracy and can be restricted without impinging on free expression brennancenter.org brennancenter.org.
European Union: The EU is moving aggressively on broad AI regulation, including measures directly relevant to deepfakes. The landmark EU AI Act, agreed upon in 2024 (set to fully apply by 2026, with some provisions earlier), includes a transparency requirement for synthetic media. Under the AI Act, any AI system that can generate “deepfake” content must ensure that the content is labeled as AI-generated (unless used in certain exempted areas like art or security research) realitydefender.com. In practice, this means developers of generative image or video models in the EU will be obliged to build in watermarking or metadata that signals the output is synthetic. Failure to do so could incur hefty fines under the Act’s enforcement. Additionally, the EU’s updated Code of Practice on Disinformation (a voluntary code that major online platforms have signed onto) specifically calls out deepfakes as a menace and commits platforms to developing “policies, measures and tools to address manipulated content” brennancenter.org brennancenter.org. For example, platforms agreed to implement systems to detect and either label or remove deepfake videos that could cause public harm, and to cooperate with fact-checkers in rapidly debunking false AI content. Under the Digital Services Act (DSA) – which came into effect in 2023 – very large online platforms in the EU must assess and mitigate “systemic risks” on their services, including the spread of AI-generated disinformation. This regulatory pressure has led companies like Meta, Google, and TikTok to announce new safeguards for the 2024–2025 European election season: from improved deepfake detection to more prominent flagging of synthetic media. In short, Europe is taking a transparency-first regulatory stance: requiring labels on AI outputs and holding platforms accountable for curbing deepfake-driven disinformation. Critics note that enforcement will be challenging (how to catch all unlabeled fakes in a flood of online content?), but the EU is signaling that unchecked deepfakes are unacceptable and not compatible with its digital governance standards realitydefender.com realitydefender.com.
United Kingdom: The UK has yet to pass deepfake-specific election laws, but it is addressing the issue through broader online safety and AI initiatives. In 2023, the UK enacted the Online Safety Act, a sweeping law aimed at regulating harmful online content. That law notably criminalized the sharing of non-consensual deepfake pornography – making it illegal to create or distribute explicit synthetic images of someone without their consent policyoptions.irpp.org. This tackled the harassment side of deepfakes. For election misinformation, the Online Safety Act empowers Ofcom (the communications regulator) to issue codes of practice on disinformation. Experts are urging Ofcom to develop a Code of Conduct on Disinformation that would include standards for handling AI-manipulated content cetas.turing.ac.uk. Such a code, possibly modeled on the EU’s approach, could push social media platforms and political actors in the UK to refrain from spreading deepfakes and to clearly label any synthetic media. There are also calls for the UK Electoral Commission to provide guidance to political parties on responsible AI use, establishing red lines against deceptive deepfakes in campaigning cetas.turing.ac.uk. In late 2024, a cross-party committee of MPs recommended tightening election laws to penalize deepfake disinformation, though formal legislation has not yet been introduced. The government has indicated it is reviewing whether existing laws (for example, those on libel, fraud, and electoral offenses) are sufficient to prosecute malicious deepfake usage or if new statutes are needed cetas.turing.ac.uk. Additionally, the UK is standing up an AI Safety Institute and hosted a global AI Safety Summit in 2023, where manipulation of information was on the agenda. British authorities appear to be focusing on improving technical defenses and media literacy (discussed below in recommendations) as much as legal bans. Still, the UK’s steps like outlawing deepfake porn and empowering regulators show an understanding that AI-driven false content requires a policy response.
Canada: As of 2024, Canada had no specific law against using deepfakes in elections. The Canada Elections Act does not explicitly prohibit AI-generated disinformation or deepfakes, meaning it would have to be prosecuted under general provisions (such as laws against fraud or impersonation) which may not be entirely adequate cef-cce.ca. This regulatory gap has been highlighted by experts who warn Canada is “a step or two behind” other democracies on this issue policyoptions.irpp.org. In the fall of 2023, Canada experienced a minor deepfake-related incident when a fraudulent audio clip circulated, falsely sounding like a politician. While it caused little impact, it raised awareness. Elections Canada (the elections authority) has since flagged AI misinformation as an emerging threat and is studying potential responses cef-cce.ca. Policy analysts are calling for new legislation “yesterday” – possibly empowering the Commissioner of Canada Elections to crack down on deceptive synthetic media in campaigns policyoptions.irpp.org. Canada can draw on ideas from allies: for example, adopting disclosure rules for AI-generated election ads, or making it an offense to spread material known to be a deepfake intended to mislead voters. As of mid-2025, no bill had been tabled, but pressure is mounting for Canada to join the ranks of jurisdictions tackling election deepfakes through law policyoptions.irpp.org.
Other Democracies: Across the world, several other democracies have begun implementing measures:
- Australia: The Australian government, concerned about AI “truth decay” before upcoming votes, announced plans for “truth in political advertising” legislation that would ban deceptive deepfake videos and audio in election campaigning innovationaus.com. The proposal, introduced by the Albanese government in 2023, would prohibit publishing synthetic media that impersonates real candidates or could mislead voters, during election periods innovationaus.com. However, the legislative process is slow – reports indicate these deepfake provisions may not come into force until 2026 innovationaus.com, meaning the 2025 federal election might proceed without them fully in effect. In the interim, Australia’s Electoral Commission has issued guidance and emphasized the need for perspective (the commission noted over-focusing on deepfakes could inadvertently reduce trust in real information) ia.acs.org.au. Australian politicians across party lines have voiced support for curbing AI disinformation, and the debate continues on how to balance it with free political speech theguardian.com sbs.com.au.
- Taiwan: After encountering deepfake interference from China, Taiwan updated its election laws. In 2023, Taiwan’s legislature amended the Election and Recall Act to specifically outlaw sharing doctored audio or video of candidates with intent to falsely influence the outcome policyoptions.irpp.org. This provided a clear legal tool to pursue perpetrators of the kind of deepfake smears seen in 2024. Taiwan also invested in public education and a rapid-response system (involving government, civil society, and tech platforms) to debunk false information, which helped mitigate the impact policyoptions.irpp.org policyoptions.irpp.org.
- European democracies: Individual European countries, aside from EU regulations, have started to address deepfakes under existing laws. For instance, France’s law against “false information” during elections (passed in 2018) could apply to deepfake videos spread with intent to skew a vote, and Germany’s strict defamation and election laws might similarly be used. But we are also seeing proposals for new measures: in Germany, officials have discussed requiring political parties to declare use of synthetic media in campaign materials. In the UK, as noted, future amendments to election law (like imprint requirements for digital ads) may include AI content disclosures cetas.turing.ac.uk.
- International initiatives: There is a growing recognition that global cooperation is needed, since disinformation crosses borders. The G7 has a working group on “AI Governance” which in 2024 issued a statement about combating the malicious use of AI in the information space. The Biden administration in the U.S. secured voluntary commitments from big AI developers (OpenAI, Google, Meta, etc.) to implement watermarking for AI content and to invest in misuse prevention. While not binding, these indicate an international norm emerging in favor of transparency and responsibility in AI usage.

In summary, policy responses to deepfakes are accelerating. Legislation is still catching up to technology, but the trajectory is clear: governments are moving to criminalize the most damaging uses of synthetic media in elections, to mandate transparency (labels/disclosures) for AI-generated content, and to empower regulators or election agencies to act against digital forgeries. At the same time, they must safeguard legitimate expression like satire and avoid draconian rules that could be misused to censor. Striking this balance is tricky. The approaches taken – from U.S. state laws to EU-wide mandates – will provide a testing ground in 2025. Policymakers will undoubtedly refine these tools as we learn more about what works. But doing nothing is not an option: as one policy tracker put it, “Without regulation, deepfakes are likely to further confuse voters and undermine confidence in elections.” citizen.org citizen.org The next section outlines strategic recommendations building on these efforts, targeting all stakeholders in the democratic process.

Strategic Recommendations for Safeguarding Elections

Defending electoral integrity in the age of AI will require a multi-pronged strategy. No single tool or law can solve the deepfake problem; instead, a coordinated effort by governments, technology platforms, media, and civil society is needed. Below are strategic recommendations across these sectors to mitigate risks and ensure voters can make informed decisions in 2025 and beyond:

Governments and Policymakers

1. Strengthen Legal Protections and Deterrence: Governments should enact or update laws to explicitly outlaw the malicious use of synthetic media in elections. This includes making it illegal to create or distribute, with intent to deceive the public or sabotage an election, any deepfake that falsely depicts a candidate or manipulates election-related information (such as voting procedures). Narrow tailoring is key – the laws should target intentional deception (disinformation), with clear exemptions for satire, parody, or obvious artistic expression. Penalties (fines or criminal charges) will create a deterrent for would-be deepfake peddlers, especially if enforced promptly. For example, Australia’s proposed ban on deceptive deepfakes during campaigns and Taiwan’s new clauses against AI-manipulated election content can serve as models innovationaus.com policyoptions.irpp.org. In the U.S., federal action (like the proposed No AI FRAUD Act) could set a baseline nationwide, complementing state laws. Additionally, governments should update campaign finance and advertising rules: require that any political ad (online or broadcast) containing synthetic media include a clear disclaimer (e.g. “This image/video is AI-generated”) so viewers are not misled. Truth-in-advertising regulations for campaigns must extend to AI content.

2. Implement Election Incident Response Protocols: Election authorities should establish formal protocols to respond to serious deepfake incidents in real time. A great example is Canada’s Critical Election Incident Public Protocol, which brings together senior officials to assess and inform the public of foreign interference or disinformation threats during an election cetas.turing.ac.uk cetas.turing.ac.uk. Other democracies should adopt similar mechanisms. If a dangerous deepfake emerges (say a fabricated video of a candidate conceding defeat circulates on Election Day), the protocol would be activated – officials, intelligence experts, and tech platforms would rapidly verify the truth and issue a public announcement debunking the fake and clarifying the facts cetas.turing.ac.uk. This rapid rebuttal capability is crucial to blunt the impact of “firehose” disinformation. Governments should practice these responses in advance (war-game various deepfake scenarios) so that they can react swiftly and with one voice when needed.

3. Invest in Detection and Authentication Infrastructure: Public sector agencies should pour resources into advancing deepfake detection and content authentication. This involves funding R&D (for example, DARPA-style programs focused on AI-mediated misinformation), supporting the deployment of detection tools for election use, and adopting authentication standards in government communications. A concrete step is for government media (state broadcasters, official social media accounts, etc.) to start adding provable provenance metadata to all official photos, videos, and audio they release cetas.turing.ac.uk. By doing so, they create a foundation of “verified genuine” information. Voters and journalists could then trust that any video with a government seal in its metadata is authentic – and conversely be more skeptical of similar footage lacking that credential. Governments can lead by example on this “authenticity-by-design” approach cetas.turing.ac.uk, which the U.S. and UK are already exploring. Furthermore, law enforcement and election oversight bodies should be equipped with forensic analysis units to evaluate suspect media during campaigns. Knowing that authorities have the technical means to trace and attribute deepfakes (and potentially identify perpetrators) will also deter malicious actors.

4. Clarify and Modernize Existing Laws: Many countries may find that current laws on fraud, identity theft, defamation, or election interference can be applied to some deepfake cases – but there may be gaps. Governments should review their legal code to see if new categories are needed. For instance, do we have statutes that cover AI-generated impersonation of a public official? If not, introduce them. Ensure that data protection and privacy laws include unauthorized AI use of someone’s likeness/voice as a violation. Clarifying the legal status of harmful deepfakes (and doing public outreach about it) is important so that potential bad actors know they can be held accountable. It also empowers victims (candidates or citizens) to pursue remedies if they are targeted. This review should also consider electoral laws: updating definitions of illegal election advertising or polling misinformation to explicitly encompass synthetic media manipulations cetas.turing.ac.uk. The goal is to remove any ambiguity – a would-be disinformer should not be able to claim “technically it’s not illegal because it’s AI.” If laws are explicit, it simplifies enforcement and prosecution.

5. Enhance International Collaboration: Because disinformation campaigns frequently originate abroad (or spread across borders), democratic governments should work together on this issue. Intelligence agencies and cybersecurity units should share information on emerging deepfake tactics observed (for example, if one country detects a foreign deepfake operation, it should warn others). Forums like the Alliance for Securing Democracy, the G7, EU-US dialogues, and others can coordinate joint statements and norms against election deepfakes. Diplomatic pressure can be applied on state actors who sponsor or tolerate such interference. There is also room for collaborative research – e.g. an international center for deepfake detection could pool data to improve algorithms. Election monitoring organizations (like the OSCE or international observer missions) should update their methodologies to look for synthetic media influence, and nations can include deepfake contingencies in mutual defense pacts for democratic processes. A united front will make it harder for malign actors to exploit any single country’s vulnerabilities.

6. Promote Public Awareness and Digital Literacy: Ultimately, governments have a role in educating the electorate about deepfakes. Many countries are now considering or rolling out digital literacy programs in schools and for the general public cetas.turing.ac.uk. These programs teach people how to verify online information, recognize signs of manipulated media, and think critically about sources. Given how convincing AI fakes have become, it’s vital that every voter knows such fakes exist and feels empowered to double-check startling content (rather than blindly believe or share it). Governments should partner with educational institutions and NGOs to include deepfake awareness in curricula and public-service campaigns. For example, running PSAs that show side-by-side real vs deepfake clips of a politician and explaining the difference can raise awareness. Evidence suggests that individuals with higher media literacy and critical thinking skills are far better at detecting deepfakes and resisting misinformation cetas.turing.ac.uk. Therefore, funding media literacy initiatives is one of the most effective long-term defenses. When the public becomes an active sensor network – spotting and calling out fakes – the impact of deepfake propaganda can be greatly diminished.

Technology Platforms and AI Developers

1. Strengthen Platform Policies and Enforcement: Social media and online platforms are the main distribution channels for viral deepfakes. These companies should adopt strict policies against manipulated media that deceives users, especially in the context of elections. Many platforms have started this: for example, Facebook and Twitter (X) have policies to remove or label “manipulated media” that could cause harm. But enforcement must be robust. Platforms should improve their automated detection of deepfakes (using the latest tools discussed earlier) and ensure rapid review by human moderators when users flag suspect content. In election periods, companies can set up special war rooms and collaboration channels with election commissions to handle potential deepfake incidents in real time. When a fake is identified, platforms should label it as false or remove it promptly, and downrank it in algorithms to curb further spread brennancenter.org brennancenter.org. Transparency is also key: platforms can publish regular reports on the deepfakes they’ve detected and what actions were taken, which builds public confidence. They should also share samples of detected deepfakes with researchers to improve collective understanding.

2. Implement Deepfake Disclosure and Tracing: Borrowing from the EU’s lead, platforms globally should require that AI-generated content is tagged and disclosed. For instance, if a political ad is uploaded that contains an AI-generated image or voice, the platform could mandate the uploader to check a box indicating “this content has synthetic elements” – and then display a notice to viewers (“This video was altered or partially generated by AI”). Even outside formal ads, platforms can use detection tools to visually mark suspected deepfake videos (e.g. a warning overlay that the video’s authenticity is unverified). In addition, social networks and messaging services might integrate content authenticity features: using standards like C2PA, they can show users an icon if an image’s source and edit history are verified, or conversely flag if that data is missing. Some tech companies (Adobe, Microsoft, Twitter) are already involved in such efforts. By baking provenance signals into their UIs, platforms can help users differentiate real from fake. They should also work on trace-back mechanisms – for example, if a harmful deepfake is spreading, can they trace who originally uploaded it, even if it’s been reposted thousands of times? Cooperation with law enforcement on major incidents (while respecting privacy laws) will be important to catch perpetrators.

3. Ban Malicious Deepfake Users and Networks: Platforms need to exercise vigilance against organized actors who repeatedly deploy deepfakes. This means not just removing individual pieces of content, but shutting down accounts, pages, or bots engaged in coordinated deepfake campaigns. If evidence links an operation to a state-sponsored effort or a known troll farm, platforms should publicize that and eliminate their presence. Many disinformation networks have been taken down in recent years; the same aggressive approach must apply to AI-fueled influence operations. Platforms should update their terms of service to explicitly prohibit the malicious creation or sharing of synthetic media to mislead others. Those rules give a basis for banning violators. In political advertising, any campaign or PAC caught using deceptive deepfakes should face penalties such as loss of ad privileges. Tech companies might also join forces to maintain a shared blacklist of notorious deepfake hashes or signatures, so that once a fake is identified on one platform, it can be blocked on others (much like how terrorist content hashes are shared via consortium). Essentially, make it unrewarding to try to use deepfakes on mainstream platforms – either the content will be swiftly removed or the actor behind it will lose their account.

4. Collaborate with Fact-Checkers and Authorities: No platform can perfectly police content alone. Collaboration is vital. Social media companies should deepen partnerships with independent fact-checking organizations to evaluate viral content. When fact-checkers debunk a video as fake, platforms need to amplify that correction – e.g. appending a fact-check article link whenever the video is shared, or notifying all users who saw the fake initially. Companies like Facebook have done this for misinformation and should continue for deepfakes. Additionally, platforms should coordinate with election commissions and security agencies, especially during election season. They can establish direct hotlines or channels for officials to report suspected deepfakes affecting voting, and likewise platforms can alert governments if they see foreign disinformation targeting the country. In some jurisdictions, formal arrangements are in place (for instance, the EU Code of Practice encourages info-sharing with governments on disinfo threats brennancenter.org). Even in the U.S., the Department of Homeland Security’s cybersecurity unit works with platforms on election disinformation monitoring. These collaborations must of course respect free expression and not cross into censorship of legitimate speech. But for clearly fabricated, harmful material, a swift, coordinated response between platforms and public institutions can stop a fake from metastasizing. This could include joint press statements debunking a viral fake or algorithms boosting authoritative sources to counteract the spread.

5. Advance AI Model Safeguards: The companies building generative AI models (OpenAI, Google, Meta, etc.) have a responsibility at the source. They should implement safeguards to prevent misuse of their AI for election interference. This might include watermarking AI outputs, as discussed (so any image generated by e.g. DALL-E or Midjourney has an embedded signature). It could also involve training data curation – for example, ensuring their models have been trained to refuse requests to impersonate real individuals in a harmful manner. Already, some AI tools won’t generate deepfake images of real political figures because of built-in content filters. These guardrails should be continually improved (though open-source models present a challenge, as they can be fine-tuned by bad actors without such restrictions). AI developers should also invest in research on deepfake detection techniques and share these with the community. It’s a positive sign that many leading AI firms have voluntarily pledged support for watermarking and content authentication. Going forward, they could collaborate on a standard API that allows any video or audio file to be quickly checked to see if it was generated by one of their models. In essence, those who create the “problem” (the generative tech) should also help create the “solution” (means to identify its output).

6. Transparency in Political Advertising: Platforms hosting political ads should enforce strict transparency around use of AI. If a campaign ad is boosted on Facebook or Google that contains AI-generated elements, the platform’s ad library should explicitly note that. The platforms could even require political advertisers to submit the raw, unedited footage for comparison. More ambitiously, social media platforms might consider temporarily banning all political ads that contain synthetic media during the sensitive final days of a campaign – similar to how some ban new political ads right before Election Day. This would eliminate the risk of a last-second deepfake ad blitz. While enforcement is tricky, the principle is that paid promotion of deceptive content is especially dangerous and platforms have more leeway to regulate advertising than user posts. Ensuring high transparency and rapid takedowns in the advertising domain is critical, since a deepfake circulated by paid ads could reach millions targeted by algorithm, distorting the information environment unfairly.

Media and Journalistic Organizations

1. Rigorous Verification Protocols: News media must adapt their verification practices to the deepfake era. Every newsroom – from national TV networks to local newspapers and fact-checking sites – should establish formal procedures to authenticate audio-visual material before broadcasting or publishing it. This includes training journalists to use forensic tools (e.g. checking video metadata, running image analyses) and to consult experts when needed. For any sensational or scandalous clip that surfaces during an election, editors should treat it with healthy skepticism and not rush to air it without confirmation. Media outlets should double-source any user-generated content: for instance, if a video emerges of a candidate doing something shocking, the outlet should seek corroborating evidence (witnesses, official statements, etc.) or at least get frame-by-frame analysis to ensure it’s not a deepfake. The goal is to avoid becoming unwitting amplifiers of disinformation. Impressively, some news organizations have started internal deepfake task forces. In one case, journalists in Arizona even created their own deepfake (with permission) to educate viewers on how easy it was to manipulate video knightcolumbia.org – a clever way to raise awareness. All newsrooms should consider having a “deepfake expert” on call (or a partnership with a tech lab) for quick analysis of suspect footage. By making verification as routine as fact-checking, media can catch fakes early or at least caution their audience if something hasn’t been verified.

2. Responsible Reporting on Deepfakes: When covering instances of synthetic media, journalists should do so carefully and with context. If a deepfake targeting a candidate goes viral, the story is not the fake claims themselves, but the fact that it’s a false manipulation. Media reports should refrain from repeating the fake allegations in detail or playing the fake video uncritically, as that can inadvertently spread the misinformation further. Instead, they can describe it generally and focus on the response (e.g. “A manipulated video falsely depicting X doing Y was released online, which has been debunked by experts”). Outlets might choose to blur or not link directly to the deepfake content in their online articles cetas.turing.ac.uk, to prevent driving traffic to it or enabling malicious users to download and repost it. The framing of the report is important: emphasize the attempt to deceive and the fact of the deepfake more than the fake narrative it contained cetas.turing.ac.uk. Media should also highlight the corrections or the truth (for example: “No, politician Z did not say this – the video is an AI fabrication; here is what they actually said on the topic.”). By consistently doing this, reputable media can help inoculate the public against believing or sharing the fake. It’s a delicate balance between covering the disinformation (since ignoring it won’t make it go away) and not accidentally amplifying it. Guidelines akin to those for reporting on hoaxes or mass shootings (where certain details are minimized to avoid copycats) could be developed for reporting on deepfakes. The Independent Press Standards Organisation in the UK has been urged to update its codes to cover such situations cetas.turing.ac.uk.

3. Use of Authenticity Technology in Newsrooms: News organizations themselves can leverage the emerging authenticity infrastructure. For instance, a media outlet could adopt the Content Authenticity Initiative’s tools to attach cryptographic content credentials to all original photos and videos taken by its journalists. This means any footage captured by, say, a Reuters or AP cameraman could carry a secure seal verifying its origin and any edits. Downstream, when people see a Reuters-sourced video, they could check that it’s unaltered. Such measures help assert what is real, offering the public a source of truth. Media outlets should also collaborate in building databases of known deepfakes (and known genuine content) that can aid fact-checkers. For example, maintaining a repository of official speeches and interviews can help quickly debunk a doctored clip by comparison. Major wire services and news agencies might coordinate to rapidly alert all their subscribers if a dangerous deepfake is spotted – a bit like how they issue breaking news alerts. Internally, news editors should also be aware that political operatives might attempt to feed fake media to journalists (e.g. a tip with a “leaked” audio that is actually AI-generated). Keeping skepticism high for any anonymously sourced digital material is prudent.

4. Educating the Audience: Media can play a big role in educating voters about synthetic media. News outlets and journalists should produce explainer pieces, interviews with experts, and segments that show the public how deepfakes are made and how to spot them. By demystifying the technology, they reduce its power. Some TV news segments in 2024, for instance, demonstrated AI voice clones on-air to reveal how a scam call might mimic your relative’s voice. Likewise, election-season coverage can include reminders: “If you see an outrageous video about a candidate at the last minute, be cautious – it could be fake. Here’s how to verify…”. Media-led public awareness campaigns (possibly in partnership with government or NGOs) could significantly boost digital literacy. Journalists should also consistently use precise language: calling something a “deepfake” or “AI-generated false video” rather than just “doctored video” helps reinforce that this new category exists. Over time, a well-informed public will be less likely to fall for a fake and more likely to demand evidence. Media, as the interface between information and the public, have a duty to build that resilience.

5. Accountability and Exposure: Finally, journalists should investigate and shine light on who is behind high-profile deepfake operations. Holding perpetrators accountable in the court of public opinion can deter future misuse. If a rival campaign, a foreign troll farm, or a specific online group is identified as the source of a malicious deepfake, reporting that prominently will attach stigma and risk to such tactics. Exposés about the production and financing of disinformation campaigns can drain their efficacy. Additionally, if a politician or public figure themselves share a deepfake knowing it’s false (for example, a candidate tweets a fake video of their opponent), media should call that out firmly – treating it as a serious misconduct. The prospect of negative press and reputational damage may discourage political actors from “dirty tricks” like deepfake use. In short, journalism’s watchdog function extends into the digital realm: investigate, attribute, and expose malicious synthetic media operations just as you would any other fraud or corruption in politics.

Civil Society and Voter Initiatives

1. Digital Literacy and Community Education: Civil society organizations – including nonprofits, libraries, universities, and grassroots groups – can lead the charge in educating citizens to navigate the deepfake era. Scalable programs should be offered to communities on how to verify media. For example, NGOs can conduct workshops that teach people simple tricks like doing reverse image searches (to see if a photo is AI-generated or has been manipulated), checking for corroborating news, and using fact-checking websites. There are already excellent toolkits and curriculums developed by fact-checking groups (e.g. First Draft, Media Literacy Now) that cover spotting misinformation and deepfakes; these should be widely disseminated. Such training should not only target students in schools but also older adults, who are often more vulnerable to online deception. Nationwide digital literacy campaigns can be rolled out, possibly with government funding but executed by community organizations for trust. The objective is to raise the “herd immunity” of society: if a critical mass of people can recognize a fake or at least suspend judgment until verification, the disinformers lose much of their power. Surveys show the public wants this knowledge – many feel anxious about not knowing how to tell real from fake brennancenter.org brennancenter.org. Civil society can fill that gap by empowering citizens through education and practical skills.

2. Fact-Checking and Debunking Initiatives: Independent fact-checkers and civil society watchdogs will remain crucial. They should gear up specifically for election periods with initiatives like dedicated deepfake fact-check hubs. For instance, coalitions of fact-checking organizations could maintain a public dashboard during an election that tracks rumors and emerging deepfake claims, providing rapid debunks. The News Literacy Project did something similar for the 2024 U.S. elections, logging misinformation cases and noting how few actually involved AI knightcolumbia.org knightcolumbia.org. This kind of transparent tracking helps the public and journalists see the big picture and not exaggerate the threat, while still addressing the real cases. Civil society groups can also push out correctives on social media – e.g. responding to viral posts with accurate info, aided by community notes or other features. We should also promote “prebunking”: warning the public beforehand that a fake might appear. For example, if intelligence or past patterns suggest a candidate could be targeted with a fake scandal, civic groups (in coordination with election officials) can alert voters: “Be skeptical if you suddenly see a shocking video of X, there’s potential for a deepfake.” Studies indicate that prebunking can significantly reduce gullibility and the spread of false claims cetas.turing.ac.uk cetas.turing.ac.uk. Thus, a proactive approach by civil society, anticipating and pre-empting deepfake campaigns, could pay dividends.

3. Civic Tech and Crowd-Sourced Detection: The tech-savvy citizen community can be mobilized to fight deepfakes. There are already “deepfake hunter” volunteers who analyze suspect media online. Civil society can organize these efforts via platforms – perhaps a dedicated portal or app where people can submit videos or audio they doubt for analysis, and a network of experts or AI tools provides an authenticity report. This crowd-sourced intelligence could supplement official efforts. Additionally, civic tech groups might develop browser plugins or phone apps that help users identify synthetic media. For instance, an app could let a user select a video on their screen and get an instant analysis from multiple detection algorithms (sort of like antivirus software for deepfakes). While not foolproof, it could raise red flags. Open-source efforts to create such tools should be supported by grants. Another idea is citizen reporting hotlines – akin to election day hotlines for voting issues, there could be a channel for people to report suspected disinformation or deepfakes they encounter, feeding into election authorities or fact-checkers who can then respond. By engaging citizens as active participants in spotting and flagging dubious content, the scale of monitoring increases vastly. This distributed approach recognizes that in a society of millions online, someone will often catch something early – the key is to channel those observations quickly to those who can verify and amplify the truth.

4. Advocacy for Platform Accountability: Civil society should continue to press technology platforms and AI companies to behave responsibly. Public interest groups and think tanks have been crucial in highlighting the dangers of deepfakes and advocating for reforms (e.g. Access Now, EFF, and others have issued recommendations). This advocacy must persist – urging platforms to implement the policy changes noted earlier (better labeling, takedowns, etc.), and pushing AI makers to adopt ethics in design. Public Citizen’s campaign to track state legislation on deepfakes and petition the FEC is one example citizen.org citizen.org. Similarly, coalitions can call for transparency from platforms: demanding they release data on how much AI content is on their site, or how effective their detection is. Civil society voices can also help ensure that any new laws or regulations properly protect civil liberties (for instance, resisting any overbroad rules that could suppress free speech under the guise of fighting deepfakes). Striking that balance requires public consultation, and advocacy groups stand in for the citizenry in those debates. The next few years may see new regulatory frameworks for AI and online content – it’s vital that democratic values and human rights principles are upheld in those, and civil society is key to that watchdog role.

5. Support for Victims and Targets: If a candidate or private individual is maligned by a deepfake, civil society can provide support. Nonprofits might offer legal assistance or advice on how to get defamatory deepfakes taken down and hold perpetrators accountable. There could be helplines for victims of deepfake porn or character assassination, connecting them with law enforcement and mental health resources. For candidates hit with a smear, civic organizations (like league of women voters or election integrity groups) can help amplify their denial and the debunk to minimize harm. Quickly rallying to the defense of someone falsely targeted – making sure the truth is louder than the lie – is something community and advocacy groups can coordinate, as they often do in countering defamation or hate speech. On a broader level, civil society can facilitate cross-party commitments that if any deepfake emerges, all sides will condemn it. Imagine a pledge that all major parties in a country sign, vowing not to use deepfakes and to swiftly denounce any malicious forgeries that appear. Such norms, fostered by groups like inter-party election committees or ethics NGOs, would reduce the likelihood of a “race to the bottom” where parties feel they must respond in kind. It creates a united front that attacks on truth will not be tolerated, regardless of whom they target.

In conclusion, meeting the deepfake challenge requires leveraging all of society’s defenses – technological, legal, institutional, and human. By executing the steps above, governments can harden the electoral system against AI fakery, tech platforms can curtail the spread of false content, media can ensure truth prevails in reporting, and citizens can become savvy guardians of reality. There is no time to waste: as generative AI continues to advance, the 2025 election cycle will test democracies’ resilience to synthetic lies. The encouraging news is that we are not defenseless. With preparation, transparency, and collaboration, we can outsmart and out-organize deepfake campaigns, preserving the integrity of our elections. As a CETaS research report on AI and elections concluded, “complacency must not creep into decision-making” – instead we should seize the current moment to build resilience cetas.turing.ac.uk cetas.turing.ac.uk. By doing so, we uphold the principle that while technology evolves, our democratic values of truth and trust will endure.

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Additional references: Reuters, Wired, and CNN articles cited within the sources above for specific incidents (e.g. Zelensky deepfake, Hong Kong $25M fraud via Zoom deepfake weforum.org), and FTC consumer alerts on voice-clone scams weforum.org. These are embedded in the analysis and available through the listed source links.