Deepfakes use AI to produce manipulated media to mislead viewers

Malicious use of deepfakes can be found in scams, misinformation, and disinformation

Artificial neural networks can identify an individual's physical characteristics when provided with training data, such as real footage of the person speaking. Graphics techniques overlay these with matching characteristics from another real or generated person to create novel footage of things that have never taken place in reality.

The ease of deepfakes creation may soon overwhelm our sense of digital truth

Shortly after the public release of related technology in 2017, some experts saw the technical limitations of deepfakes as preventing them from becoming widespread tools of disinformation. Since then, the neural networks behind diffusion models and generative AI have eliminated the barriers to creating convincing synthetic media for propaganda.

Deepfakes came to prominence in the form of nonconsensual pornography

The explosion of deepfakes depicting renowned people in both ridiculous and compromising videos has provoked widespread fears they will undermine public trust in authoritative knowledge. However, some scholars argue that real videos will always be distinguishable from deepfakes via the video-sharer's testimony and a broader social context.

Deepfake potential ranges from destabilizing democracies to improving accessibility

The technology can revolutionize education, medicine, and art through actions such as helping a disabled person express themselves. However, fictional audio and video can also jeopardize a person's reputation and life or quickly spread misinformation, even without content being flawless recreations.

Once limited to expert studios, automated tools can create convincing deepfakes

Although the best deepfakes are engineered using generative adversarial networks where systems compete to create and assess fake data until the two cannot be distinguished, simpler tools have allowed nonexperts to develop deepfakes for financial scams, political deception, and nonconsensual pornography.

AI-generated avatars allow users to create deepfakes in real time

Synthetic media tools can map voices and faces to make individuals appear as someone else. Although they open the door to new opportunities in immersive education and entertainment, they also raise concerns about the misuse of personal identity.

Surreal backgrounds, asymmetry, and indecipherable text can help identify deepfakes

Differences in the backgrounds of training data with the same subject introduce variability, resulting in background textures and text that lack realistic detail. Difficulties with managing long-distance dependencies, the complexity of hair, and other real-world phenomena introduce errors that can be used to flag AI-generated content.

Deepfake videos impersonating real doctors are spreading false medical advice

More than 100 videos, found primarily on Instagram and TikTok in August 2025, promoted products related to beauty, wellness and weight loss. While TikTok removed 94% of content violating its policies on AI-generated content, these deepfakes were watched and shared before removal, undermining public trust in medicine.

Deepfake pornography poses significant challenges in combating sexual abuse

In 2017, 96% of deepfake videos involved image-based sexual abuse, which has disproportionately targeted minority women, LGBTQ individuals, adolescents and celebrities. Cultural norms, misogyny and AI advancements have contributed to the proliferation of explicit deepfake content.

Audio deepfakes have fooled voice biometrics at banks

Limitations in facial and body movements as well as mismatches between audio and lip movements can sometimes help flag a video as a deepfake, but removal of the video component significantly improves AI mimicry.

Take a quiz to identify whether you can spot deepfakes

Advances in artificial intelligence have made deepfakes increasingly difficult to identify, as they are intentionally designed to appear real. The technology has exacerbated some of the problems brought on by digital editing tools like Photoshop.

Digital re-aging and de-aging of faces in film and TV has become increasingly common

Traditional techniques for accomplishing this process were slow and labor-intensive, and often required manual frame-by-frame edits, forcing them to be reserved for "hero characters" and close-up shots. Deep learning methods used by Disney and other production companies now provide fully-automatic and production-ready methods to produce such deepfakes for entertainment purposes.

Governments are now using generative AI to manipulate public opinion

A Freedom House report found that AI-powered disinformation campaigns and censorship tactics are spreading, with 16 countries utilizing the technology to shape online narratives or suppress dissent, as of 2023. In Venezuela, deepfake software was used to push pro-government propaganda with fake news anchors.

In China, deepfaking a dead loved one has become a growing industry

Digital replicas of the deceased provide individuals with another way to process grief in a culture with long-standing traditions of communicating with ancestors. The emotional and ethical implications of "digital immortality" are complicated further by cases of actors impersonating deceased relatives during deepfake video calls to hide their deaths.

In 2024, deepfake audio of London Mayor Sadiq Khan nearly caused real-world disorder

The audio included incendiary remarks related to the ongoing Palestinian protests and played on the public's preexisting political biases. The technology has since destabilized the concept of truth in elections, where politicians blame the use of real gaffes on AI and deepfakes to spread misinformation.

Warren Buffett has worried that AI might end up being as consequential as the development of the nuclear bomb

Berkshire Hathaway chairman Warren Buffett is one of the many people who’ve come face-to-face with the downside effects of AI, and in particular, deepfakes. He was recently shown a deepfake of himself in a video, which caused him to worry about how bad actors will take advantage of the technology.

Listen to a deepfake robocall of President Joe Biden

Bad actors are not solely using deepfake technology to manipulate images. They can also manipulate audio for political purposes. In this example, eligible voters are asked to skip the 2024 New Hampshire Democratic primary.

The AI that creates any picture you want and how it works

Since January 2021, advances in AI research produced a plethora of deep-learning models capable of generating original images from simple text prompts. Researchers at OpenAI, Google, Facebook, and others have developed yet to be released text-to-image tools, and similar models have proliferated online. This video is a primer on how we got here, how the technology works, and its implications.

AI is not good at understanding satire

Social media has become rife with misinformation. In response, some organizations have deployed AI-assisted programs trying to curb the spread of this inaccurate content. The problem is that misinformation and satire, rhetorically speaking, often use the same kind of language, something AI struggles to understand.

Watch LinkedIn cofounder Reid Hoffman interview his deepfake doppelgänger

LinkedIn cofounder Reid Hoffman created an AI doppelgänger of himself in 2024 that was fed a sample of his voice and trained on his books and other writings and speeches. In this conversation, his deepfake version attempts to replicate human mannerisms, including scratching their nose and getting distracted.

Watch Billy Joel deepfake himself in the 'Turn the Lights Back On' music video

Though deepfakes are often synonymous with manipulation and bad intentions, some artists have found creative uses for the technology. In this example, Billy Joel depicts himself in various eras of his career in keeping with the song's nostalgic message.

Indirect prompt injection is a major security flaw of generative AI systems

These attacks can manipulate AI behavior by hiding instructions in websites and PDFs that become part of training data. The more companies connect sites, services, and sensitive datasets to AI tools, the greater the chances of exposure to malicious code.

Diffusion models generate nontext media by adding and removing noise from a dataset

Unlike text-based AI tools, which rely on large language models to recognize patterns in text, diffusion models use a reward system to generate images, video, audio, and 3D models. They can also be customized to match a user’s style.

DeepSeek is a Chinese AI startup that rattled US competitors when it made an AI model much faster and cheaper than they did

DeepSeek unveiled an AI model in 2025 that it said was made in two months for roughly $6M. For context, OpenAI has suggested it took over $100M to make its AI model GPT-4. The news rattled the stocks of American tech companies, including leading chipmaker Nvidia.

Unlike the models made by large AI companies, open-weight models can have their safeguards removed or weakened, a process that has become increasingly easy and may expose users to harmful information.

This removal is made possible by developers making the parameters behind their models publicly known. By adjusting these parameters, users can modify how the model processes and provides information. Many of these models can be downloaded locally, preventing developers from seeing how people are using them.

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