AI disinformation in 2026 elections: the deepfake war and how to fight it

01How deepfakes are being used in elections

Deepfakes — AI-generated synthetic media that convincingly depict people saying or doing things they never did — have moved from novelty to weapon. Deepfakes are images, videos, or audio that have been edited or generated using artificial intelligence, AI-based tools or audio-video editing software. They may depict real or fictional people and are considered a form of synthetic media, that is media that is usually created by artificial intelligence systems by combining various media elements into a new media artifact. In the 2024 US election cycle, a deepfake audio clip of President Biden urging New Hampshire voters not to vote in the primary reached thousands of voters via robocall. The perpetrator was later fined $1 million by the FCC, but the damage was done: the call created confusion and suppressed turnout.

By 2026, the technology has improved dramatically. Real-time deepfake video, once requiring hours of processing on expensive hardware, can now be generated on consumer GPUs in seconds. A deepfake of a candidate making inflammatory statements can be produced, posted, and reach millions of viewers before any fact-check can be applied. The speed of creation has outpaced the speed of verification, and this asymmetry is the core problem.

The tactics have diversified. Beyond outright fabrication, bad actors use "cheapfakes" — crudely edited clips that are technically not AI-generated but are designed to mislead — and "shallowfakes," which use AI to alter context (dubbing a real video with a fake translation, for example). These are easier to produce than full deepfakes and harder to detect because the underlying footage is genuine.

02The scale of AI-generated disinformation

Disinformation is false or misleading information deliberately spread to deceive people, or to secure economic or political gain and which may cause public harm. Disinformation is an orchestrated adversarial activity in which actors employ strategic deceptions and media manipulation tactics to advance political, military, or commercial goals. Disinformation is implemented through coordinated campaigns that "weaponize multiple rhetorical strategies and forms of knowing—including not only falsehoods but also truths, half-truths, and value judgements—to exploit and amplify culture wars and other identity-driven controversies." The scale of AI-generated disinformation in 2026 is unprecedented. According to the AI Forensics Group, over 15,000 deepfake videos targeting political figures were detected in the first half of 2026 alone — a 900% increase from the same period in 2024. These are only the detected cases; the true number is likely far higher.

The amplification problem compounds the creation problem. A single deepfake posted to a fringe account can be picked up by bot networks, boosted by algorithmic recommendation systems, and reach mainstream visibility within hours. Research from MIT found that false news stories on social media spread six times faster than true ones, and AI-generated content exploits this dynamic even more effectively because it can be produced at infinite scale with minimal cost.

State-sponsored disinformation campaigns have adopted AI as a force multiplier. The EU's East StratCom Task Force reported that pro-Russian information operations using AI-generated content increased fivefold between 2024 and 2026, targeting elections in France, Germany, and the Baltic states. These operations use AI not just for deepfakes but for generating persuasive text content, managing networks of fake accounts, and automating real-time responses to breaking news.

03Detection tools and their limitations

Detection technology has improved but remains in an arms race with generation technology. Current approaches fall into several categories: CNN-based visual models that detect artifacts in individual frames, spectrogram analysis that identifies synthetic audio patterns, multi-modal systems that cross-reference video and audio, and ensemble approaches that combine multiple detection methods.

The fundamental limitation is that detection is reactive. Every time a detection method is published, generative AI can be trained to evade it. This "arms race" dynamic means detection accuracy degrades over time as generation models improve. The best ensemble systems achieve 96% accuracy on current deepfakes, but this drops to 70-80% on deepfakes produced by the next generation of models.

Content provenance offers a complementary approach. The Coalition for Content Provenance and Authenticity (C2PA), backed by Adobe, Microsoft, and Google, has developed standards for cryptographically signing media at the point of capture. If cameras and editing software embed a verifiable chain of custody, consumers can check whether content has been altered. The limitation is adoption: provenance only works if the entire content creation ecosystem participates, and there is no mechanism to force bad actors to sign their fabrications.

04What platforms are doing about deepfakes

Major platforms have implemented varying responses. Meta requires political ads containing AI-generated content to carry disclosure labels and has deployed automated deepfake detection across Facebook and Instagram. X (formerly Twitter) has introduced Community Notes, a crowdsourced fact-checking system, but has reduced its internal trust and safety teams. YouTube requires creators to disclose "altered or synthetic content" and uses AI to detect policy violations. TikTok has banned deepfakes of private individuals and requires labels on AI-generated content depicting public figures.

The effectiveness of these measures is debated. A 2025 study by the Stanford Internet Observatory found that disclosure labels on deepfakes reduced sharing rates by only 12% — many users either do not notice the labels or do not understand their significance. More critically, enforcement is inconsistent: content flagged on one platform often reappears on another, and cross-platform coordination remains minimal.

05The role of AI in generating fake news at scale

AI enables disinformation at a scale previously impossible. A single operator can now generate hundreds of unique articles, social media posts, and comments per day using large language models. These can be distributed across networks of fake accounts managed by automation software. The cost per piece of content has dropped from hundreds of dollars (using human writers) to fractions of a cent using AI.

As artificial intelligence (AI) has become more mainstream, there is growing concern about how this will influence elections. Potential targets of AI include election processes, election offices, election officials and election vendors. There are also global efforts to improve elections using AI. The 2026 election cycle has seen the deployment of AI "astroturfing" — automated generation of fake grassroots support. Networks of AI-powered accounts post coordinated messages, reply to each other to simulate organic conversation, and amplify selected narratives. Detection of these networks requires sophisticated graph analysis to identify coordinated behavior patterns, but the accounts are designed to mimic genuine user behavior closely enough to evade many automated filters.

The text generation arms race is particularly concerning because it is cheaper and faster than video deepfakes. While deepfake video requires significant compute, AI-generated text can be produced at near-zero marginal cost. A malicious actor with access to an API can generate thousands of unique, persuasive, contextually relevant posts per hour, each tailored to specific demographics or local issues.

06How voters can identify manipulated content

Individual media literacy remains the last line of defense. Practical steps include: verifying the source before sharing (check whether the account is verified, how long it has existed, and its posting history); cross-referencing claims with multiple independent sources; being skeptical of emotionally charged content, which is designed to trigger sharing before verification; and using reverse image search to check whether images have been manipulated or taken out of context.

For video content, specific deepfake artifacts can sometimes be spotted: inconsistent lighting or shadows, unnatural eye movements or blinking patterns, discontinuities at the edges of the face, and audio-video desynchronization. However, as generation models improve, these artifacts are becoming less visible. The most reliable approach is to treat any surprising or inflammatory political content as potentially fabricated until verified by trusted sources.

Organizations like the News Literacy Project and Common Sense Media have developed educational resources specifically for election deepfakes. Several universities offer free online courses on digital forensics and media literacy. The challenge is reach: these resources must reach the populations most vulnerable to disinformation, who are often the least likely to seek them out.

07What regulation and media literacy efforts look like

Regulatory responses vary by jurisdiction. The EU's AI Act, fully effective since 2026, requires providers of AI systems that generate synthetic content to mark it as artificially generated and mandates disclosure for deepfakes. Violations carry fines of up to 7% of global revenue. The US has no comprehensive federal law, but several states have passed deepfake disclosure requirements for election content, and the FCC has used its authority over robocalls to penalize deepfake audio.

The US AI Civil Rights Act, introduced in 2025, would require disclosure of AI-generated content in political advertising and establish liability for deepfakes that cause harm. It remains in committee. At the state level, California, Texas, and Minnesota have criminalized distribution of deceptive deepfakes of candidates within 30-90 days of an election, though enforcement has been limited.

Internationally, the G7 has established a framework for coordinating responses to AI-enabled disinformation, and UNESCO has published guidelines for regulating AI in electoral contexts. However, binding international agreements remain elusive, and authoritarian states actively use AI disinformation as a tool of foreign policy, creating a fundamental asymmetry between democracies that seek to regulate and adversaries that weaponize.