{"id":8527,"date":"2024-02-21T10:00:00","date_gmt":"2024-02-21T15:00:00","guid":{"rendered":"https:\/\/cntiwpedev.wpenginepowered.com\/?p=8527"},"modified":"2025-10-06T16:06:14","modified_gmt":"2025-10-06T20:06:14","slug":"synthetic-media-deepfakes","status":"publish","type":"post","link":"https:\/\/cnti.org\/issue-primers\/synthetic-media-deepfakes\/","title":{"rendered":"Synthetic Media & Deepfakes"},"content":{"rendered":"\n
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CNTI’s Summary<\/h2>\n\n\n\n

Deepfakes, a form of synthetic media content that uses artificial intelligence (AI) to create realistic depictions of people and events, have proliferated in recent years. There are many questions about how this content affects journalists, fact-based news and mis- and disinformation. In addressing these concerns it is important to consider freedom of expression and safety. Relatedly, policies targeting deepfakes must be clear about what types of content qualify as such.<\/p>\n\n\n\n

Detection technologies and provenance approaches are being rapidly developed but it is unlikely they can prevent all potential harms by AI-altered content. Additional research should consider (1) what effects deepfakes have on journalism, (2) how content labeling addresses concerns about deepfakes (and what types are most effective), (3) what international standards should be applied to content to confirm its authenticity and (4) how best to teach the public to identify synthetic media.<\/p>\n\n\n\n

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The Issue<\/h2>\n\n\n\n

Manipulated imagery<\/a> has been around for over 150 years but it has reached a new level<\/a> with \u201cdeepfakes.<\/a>\u201d The term \u201cdeepfake\u201d originated in 2017 to describe audio and videos manipulated<\/a> with the assistance of artificial intelligence<\/a> (AI) to resemble a real person even when the person portrayed did not say or do what is depicted in the content. Deepfakes are a subset of \u201csynthetic media<\/a>\u201d which include audio, images, text and video content created with the assistance of AI. There continue to be conversations about how to differentiate synthetic audiovisual content from deepfake audiovisual content. Like many topics CNTI covers, the definitional clarity of these terms remains a work in progress and is important when considering policy.  <\/p>\n\n\n\n

The number of deepfakes online increased<\/a> tenfold<\/em> from 2022 to 2023. While some<\/a> research<\/a> raises questions about the degree to which harm can be directly attributed to manipulated media, there is certainly some evidence of it in countries such as Slovakia<\/a>, the United Kingdom<\/a> and the United<\/a> States<\/a>. This is especially alarming<\/a> amid a record-breaking number of national elections<\/a> being held in 2024 and as broader global<\/a> concerns grow about threats to the overall stability<\/a> of a country. A March 2022 deepfake<\/a>, for example, depicted the Ukrainian President falsely ordering his country\u2019s military to surrender. <\/p>\n\n\n\n

To date, the highest-quality, most-convincing deepfakes<\/a> require a large amount of training data and a lot of time to be persuasive. Therefore, most actors intending to cause harm are likely to pursue less resource-intensive means<\/a> to spread false narratives such as disinformation tactics discussed in a separate CNTI issue primer<\/a>. But as technological innovations<\/a> advance, deepfakes are rapidly becoming easier to make and more persuasive.<\/p>\n\n\n\n

Alongside the worry about the direct impact of a deepfake is concern<\/a> about a \u201cliar\u2019s dividend<\/a>\u201d and the sowing of further distrust<\/a> in \u201creal news,\u201d the news media<\/a> and government figures.<\/p>\n\n\n\n

Responses to the growth in deepfakes are occurring on several fronts. Many digital and online platforms (e.g., Facebook, Instagram, TikTok, X and YouTube) have begun implementing disclosure<\/a> policies<\/a> that require advertisements using AI-created content to be labeled. Others are banning<\/a> certain types of synthetic material, creating training<\/a> resources<\/a> to assist<\/a> in combating these images and working on ways to embed content with tags<\/a> to confirm authenticity<\/a>. <\/p>\n\n\n\n

News organizations have also developed online training courses<\/a> to assist with identifying<\/a> deepfakes. However, interviews<\/a> with expert fact-checkers reveal that while deepfakes are a concern, many feel that more critical threats come from text, images, audio and\/or video taken out of context (i.e., \u201cdecontextualized\u201d) as well as other forms of manipulated media such as \u201ccheap fakes<\/a>\u201d where information is recontextualized into a false narrative. <\/p>\n\n\n\n

Methods<\/a> to detect synthetic media have advanced over the last several years. These identification technologies<\/a> examine the shadows, geometry, pixels and\/or audio anomalies of suspected synthetic media and look for hidden watermarks<\/a> to evaluate authenticity. Detection<\/a> challenges<\/a> have encouraged researchers and the public to experiment with new ways to accurately identify<\/a> deepfakes. While these techniques will almost certainly be unable to fully<\/a> erase<\/a> the threats of synthetic media, they do offer steps toward establishing guardrails to protect the public\u2019s access to authentic information. To identify how to best implement detection methods, further collaboration across various sectors (e.g., technology, communications, policy, government, etc.) is needed. <\/p>\n\n\n\n

Most countries do not have specific legislation on synthetic media, but among those that do, policies fall into two general categories: (1) banning all deepfake content that does not obtain consent from the individual(s) depicted or (2) requiring disclosure and\/or labeling of deepfake content. Deepfake-related regulation is particularly complex due to many countries\u2019 protections for freedom of speech and expression. <\/p>\n\n\n\n

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What Makes It Complex<\/h2>\n\n\n\n

Clearly delineating what qualifies as a deepfake is difficult but critical.<\/strong> Governments and researchers are confronting<\/a> how best to differentiate<\/a> deepfakes from other types of synthetic media content. For instance, there is a question about whether the material depicted must be deceptive<\/em> in nature to be classified as a \u201cdeepfake.\u201d Other definitional considerations revolve around intent<\/em>, harm<\/em> and consent<\/em><\/a>. For example, a 2019 synthetic video<\/a> of soccer star David Beckham speaking nine languages<\/a> was intended to disseminate factual<\/em> information about malaria but it used deepfake technologies to make the dialogue sound authentic. The intent of this deepfake was not<\/em> to be deceptive or spread harm but it is still widely considered a deepfake because of how it was made. On the flip side, altered images<\/a> have been around<\/a> for over 150 years \u2013 without the use of artificial intelligence \u2013 but may be intentionally deceptive, similar to AI-generated deepfakes. The spectrum of synthetic media becomes increasingly complex with the inclusion of \u201cshallowfakes<\/a>\u201d and \u201ccheap fakes<\/a>\u201d \u2013 forms of manipulated media that do not require advanced technological tools. Better delineating what types of content are classified broadly as synthetic media versus specifically as \u201cdeepfakes\u201d (a subset of synthetic media) is crucial to separate the benign and beneficial uses (e.g., for education and entertainment) from harmful uses.<\/p>\n\n\n\n

While developing software to detect and counter deepfakes requires strong digital infrastructure and financial resources that only certain countries have available, new labeling and disclosure tools are making methods for addressing deepfakes more accessible globally.<\/strong> Developing independent software for detecting and countering deepfakes is expensive, but tools<\/a> to identify<\/a> whether or not media sources have been manipulated<\/a> are becoming available<\/a> for wider use. One potential strategy is to use trained<\/a> human graders in combination with pre-trained AI detection models<\/a>. Researchers find these combined approaches can have advantages over using a single detection method. In response to the growing number of deepfakes, content creators and the technology industry have also begun developing ways to tag and label manipulated media. These include both direct and indirect<\/a> disclosure approaches to maintain transparency and assert provenance (i.e., authenticity) as well as different types of content labeling<\/a>. Watermarking is one technique that can be visible to users or embedded in media to certify its authenticity. Arriving at a global standard for this type of labeling should be a priority. The Coalition for Content Provenance and Authenticity (C2PA<\/a>) is one possible standard which has received support from Adobe, Google, Intel and Microsoft among many other organizations. While technology tools and disclosure and labeling requirements greatly help to address deepfakes, they are unlikely to remove all mis- and disinformation from the news ecosystem so understanding how to mitigate<\/a> threats from all sources is critical for promoting fact-based news. <\/p>\n\n\n\n

Efforts to regulate deepfake content must be compatible with laws protecting freedom of speech and expression.<\/strong> Governments need to determine where to draw the line between legal and illegal deepfake content. In countries that legally protect free speech, deepfakes present difficult<\/a> circumstances for determining what is legal or illegal content. To a degree, sharing false statements is a protected right under freedom of speech and expression laws so banning all <\/em>deepfake content is likely illegal<\/a>, thus making the regulation of deepfakes particularly complex<\/a>. <\/p>\n\n\n\n

Synthetic media create opportunities for journalists to protect their identity in threatening situations, but deceptive behavior runs counter to many news outlets\u2019 codes of ethics on misrepresentation.<\/strong> The technological<\/a> innovations<\/a> brought forth by deepfakes may allow journalists and\/or their sources to remain anonymous by altering their appearance and voice when working on sensitive projects. However, this goes against many<\/a> outlets\u2019<\/a> codes about journalists remaining honest<\/a> and transparent during reporting as well as policies<\/a> about deception. While news organizations may outline rare circumstances for journalists to protect anonymity or to engage in deceptive practices, these are only allowed for matters of public interest or personal safety. Determining when journalists can use deepfakes for their work is an important ethical consideration.<\/p>\n\n\n\n

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State of Research<\/h2>\n\n\n\n

While deepfakes are a relatively new technological innovation, much research<\/a> has explored how individuals interpret false information presented in differing forms of media (e.g., text, audio and\/or video). In one recent study<\/a>, even in the presence of a warning that deepfake content would be encountered, nearly 80% of participants did not correctly identify the only deepfake in a series of five videos. Other work<\/a> has found that up to half of respondents in a nationally representative sample cannot differentiate between manipulated and authentic videos. These findings indicate that deepfakes are difficult to counter and that correcting false information is needed to support a well-informed public. <\/p>\n\n\n\n

There are also evidence-based areas of hope. Research suggests people can<\/em> be trained to better detect<\/a> deepfakes. Interventions that focus on the accuracy<\/a> of information and the low cost<\/a> of producing consumer-grade deepfakes (which can now be done in a matter of minutes using apps and websites) show positive results in an effort to counter the negative effects of this type of content. <\/p>\n\n\n\n

To complement training individuals about detecting deepfakes, one strategy is to study how responsive individuals are to informational fact checks<\/a> and labels<\/a> on manipulated media: <\/p>\n\n\n\n