What is Deepfake? - AI Glossary

Deepfakes are highly realistic, digitally manipulated, or synthetically generated videos, images, or audio recordings created using artificial intelligence (AI), specifically deep learning techniques, to convincingly mimic a real person’s appearance, voice, or actions.

The term itself is a blend of “deep learning” (the AI method used) and “fake,” perfectly capturing its essence: sophisticated fakes powered by advanced AI.

This technology isn’t just a far-off sci-fi concept; it’s here now, evolving rapidly, and impacting everything from entertainment and politics to personal security and global finance.

What Exactly Is a Deepfake?

At its heart, a deepfake is a piece of synthetic media. Think of it like digital puppetry, but instead of strings, complex AI algorithms are pulling the levers. These algorithms learn how a person looks, moves, speaks, and expresses themselves, and then use that knowledge to create new content where that person appears to say or do things they never actually did.

The results can range from harmless fun, like swapping faces in a funny video clip, to incredibly serious and damaging fabrications, like fake political statements or non-consensual explicit videos. The defining characteristic is the use of deep learning, a type of AI that excels at finding patterns in vast amounts of data, which allows these fakes to achieve a startling level of realism that traditional photo or video editing often struggles to match.

While the idea of manipulating images isn’t new (think Photoshop), deepfakes represent a quantum leap because the AI does the heavy lifting, enabling the creation of convincing moving images and audio with less manual effort and technical artistry than ever before – though high-quality deepfakes still require significant skill and computing power.

How Are These Fakes Born?

Creating a deepfake isn’t usually a simple button-press (though tools are getting easier to use). It typically involves sophisticated AI models and a multi-step process:

Data Collection: The process starts by gathering a lot of data about the target person – the one whose likeness will be faked – and often a source person (whose actions might be used as a base). This means collecting many images or video frames showing the person’s face from various angles, with different expressions, and under different lighting conditions. For voice deepfakes, hours of audio recordings are needed. The more high-quality data, the more convincing the final fake. Social media, YouTube videos, and publicly available interviews are common sources.
Training the AI Model: This is where the “deep learning” happens. Two main types of AI architectures are commonly used:
- Generative Adversarial Networks (GANs): Introduced by Ian Goodfellow in 2014, GANs are a cornerstone of deepfake creation. Think of a GAN as a pair of AIs competing against each other. One AI, the Generator, tries to create fake images (e.g., the target person’s face). The other AI, the Discriminator, acts like a detective, trying to tell the difference between the Generator’s fakes and real images from the dataset. The Generator constantly learns from the Discriminator’s feedback, getting better and better at creating fakes that can fool the Discriminator (and ultimately, us). This adversarial process continues until the generated fakes are highly realistic.
- Autoencoders: These are another type of neural network used, especially for face-swapping. An autoencoder learns to compress an image into a lower-dimensional representation (encoding) and then reconstruct it back (decoding). For deepfakes, two autoencoders might be trained – one on the target’s face and one on the source’s face, often sharing the encoding part. By feeding the encoded features of the source face into the decoder trained on the target face, the system can generate an image of the target person mimicking the source’s expressions and pose.
Generation & Refinement: Once the AI model is trained, it can generate the deepfake content – swapping faces frame-by-frame in a video, manipulating facial expressions, or synthesizing speech. The initial output often needs refinement. This might involve further training, manual adjustments using video editing software, or using other AI tools to improve details like lighting consistency, skin texture, and synchronization between visuals and audio.

Surprising ‘Good’ Side of Deepfakes

While the headlines often focus on the negative, deepfake technology itself is neutral; it’s how it’s used that matters. There are several legitimate and potentially beneficial applications:

Entertainment and Film: Deepfakes offer powerful tools for visual effects. They can be used for de-aging actors (like in “The Irishman”), digitally recreating actors who have passed away (like Paul Walker in “Fast & Furious 7”), face-swapping stunt doubles with actors seamlessly, or even creating entirely synthetic characters. Voice cloning can help fix dialogue in post-production or even translate films into different languages using the original actor’s voice.
Accessibility and Education: Imagine historical figures realistically delivering lessons, or people who have lost their voice using a synthesized version based on past recordings. Voice cloning can also help personalize voice assistants. David Beckham famously used deepfake technology in the “Malaria Must Die” campaign to deliver an anti-malaria message convincingly in nine different languages, vastly increasing its reach.
Art and Satire: Artists are exploring deepfakes as a new medium for creative expression and commentary. Satirical deepfakes, when clearly labeled, can be a form of political commentary or parody (though the line between satire and misinformation can be thin).
Personalized Media: In the future, we might see applications in gaming where players can insert themselves into the game, or personalized advertising (raising its own ethical questions).

These positive uses highlight the potential of the underlying technology, but they are often overshadowed by the significant risks associated with its misuse.

The Dark Side: When Fakes Cause Real Harm

The ease with which convincing fakes can be created poses serious threats across multiple domains:

Misinformation and Propaganda: Deepfakes are potent weapons in information warfare. Imagine a fake video of a world leader declaring war, a politician admitting to a crime they didn’t commit, or an expert spreading dangerous health misinformation. Deepfakes of figures like Ukrainian President Zelenskyy and Russian President Putin have already surfaced in the context of the war in Ukraine. Such fakes can manipulate public opinion, incite violence, interfere in elections, and erode trust in legitimate news sources. The potential to destabilize politics and international relations is immense.
Fraud and Financial Scams: Criminals are weaponizing deepfakes. Voice cloning allows scammers to convincingly impersonate loved ones in distress (“grandparent scams”) or company executives authorizing fraudulent transactions. In one high-profile case, fraudsters reportedly used deepfake voice technology to impersonate a CEO and tricked an employee into transferring $35 million. Another case involved a $25 million loss through similar means. Deepfakes can also be used to bypass voice or facial recognition security systems (account takeovers) or create synthetic identities for loan applications. Eftsure reports that deepfake-related fraud costs businesses nearly $500,000 on average in 2024.
Non-Consensual Explicit Content: One of the earliest and most disturbing uses of deepfakes was creating fake pornographic videos by swapping celebrities’ faces onto performers’ bodies. This has expanded to target ordinary people, predominantly women, often as a form of revenge, harassment, or blackmail. A 2019 report by Sensity AI found that a staggering 96% of deepfake videos online were non-consensual pornography. This violation of privacy and dignity can have devastating psychological impacts. Recent incidents involving students using apps to create fake nude images of classmates highlight the spread of this abuse into schools, as reported by the NEA.

Reputation Damage and Harassment: Beyond explicit content, deepfakes can be used to fabricate videos or audio showing someone saying offensive things, engaging in illegal activities, or behaving inappropriately, causing severe damage to personal and professional reputations. The Pikesville High School principal incident, where a fake racist audio recording went viral, demonstrates this danger vividly.
Erosion of Trust: Perhaps the most insidious long-term danger is the erosion of trust in all digital media. If any video or audio recording could potentially be fake, how can we believe what we see and hear? This “liar’s dividend” effect means even authentic evidence could be dismissed as a potential deepfake, making it harder to hold people accountable or agree on basic facts.

Alarming Rise of Deepfakes (By the Numbers)

Statistics paint a stark picture of how rapidly deepfake technology is proliferating and causing harm:

Explosive Growth: The number of deepfake videos online has surged dramatically. One estimate cited by Artsmart.ai suggests a 550% increase between 2019 and 2024, reaching over 95,000 videos. Surfshark’s research indicates incidents nearly doubled from 2022 to 2023 and then grew another 257% by 2024, with the first quarter of 2025 already surpassing the 2024 total.

Widespread Exposure: Deepfakes are no longer obscure. Jumio data (via Eftsure) found 60% of consumers encountered deepfake content in the last year. McAfee (via Spiralytics) found 26% encountered a deepfake scam online in 2024, with 9% falling victim.
Fraud is Rampant: Deepfake fraud attempts surged, with Onfido reporting a 3,000% rise in 2023 (via Eftsure). Sumsub data (via Security.org/Spiralytics) showed a tenfold increase in detected deepfakes globally across industries from 2022 to 2023, with North America seeing a shocking 1,740% increase. Businesses are feeling the pain, with average losses approaching $500,000 per incident (Eftsure).
Targeting Trends: While anyone can be targeted, public figures are prime victims. Surfshark’s 2025 Q1 data shows politicians and celebrities (like Elon Musk and Taylor Swift) are frequently targeted, primarily for political content and fraud, respectively. However, the general public is also increasingly targeted, especially for explicit content and fraud.
Format Prevalence: According to Surfshark, video deepfakes are the most common format overall (used heavily for fraud and political content), followed by images (dominated by explicit content creation), and then audio (often used for fraud and political messages).
Public Concern: People are worried. Jumio found 72% of consumers worry about being tricked by deepfakes and want more regulation. A 2023 McAfee survey (via Security.org) found 70% of people weren’t confident they could distinguish a real voice from a cloned one.

These numbers underscore the urgency of addressing the deepfake challenge.

How to Spot a Deepfake

As deepfakes become more sophisticated, telling them apart from reality gets harder. Our intuition isn’t always reliable. Studies show human accuracy in detecting deepfakes is often barely better than chance:

Image detection accuracy averages around 62% (Eftsure).
Video detection accuracy can be lower, sometimes cited around 57% (PNAS via Artsmart.ai), with one study finding only 24.5% accuracy for high-quality fakes (IEEE via Eftsure).
Voice clone detection accuracy is around 73% (Spiralytics).

However, deepfakes aren’t perfect (yet). Here are some potential tell-tale signs to look out for, compiled from sources like the MIT Media Lab and Built In:

Unnatural Eye Movements: Look for odd blinking patterns (too much, too little, or not synchronized) or strange gaze directions.
Awkward Facial Expressions: Emotions might seem slightly off, exaggerated, or inconsistent with the context. Lip-syncing might be imperfect or poorly synchronized with the audio.
Inconsistencies in Appearance: Skin might look too smooth or unnaturally textured. Hair might look unrealistic or strands might behave oddly. Look closely at the edges of the face, hair, or body – you might see blurring, distortion, or awkward transitions where the fake meets the real background. Moles or other distinguishing features might look off or be missing.
Lighting and Shadows: Shadows might fall incorrectly based on the surrounding environment, or lighting on the face might not match the rest of the scene. Glare on glasses might look unnatural or not change correctly as the head moves.
Audio Quality Issues: The voice might sound robotic, lack emotional variation, or have strange background noise or artifacts.
Awkward Body Posture or Movements: Sometimes the head or face might seem slightly misaligned with the body, or movements might appear jerky or unnatural.
Context Matters: Does the situation seem plausible? Would this person realistically say or do this? Always consider the source and look for corroborating information.

No single sign is definitive proof, but a combination of these anomalies should raise suspicion.

The Fight Against Fakes: Detection Tech and What We Can Do

Combating deepfakes requires a multi-pronged approach involving technology, regulation, and individual vigilance:

AI-Powered Detection: Researchers are developing AI tools to detect deepfakes automatically. These tools analyze videos and images for subtle artifacts, inconsistencies in pixel patterns, unnatural physical cues (like blood flow simulation in faces), or other digital fingerprints left by generation techniques like GANs. Technologies like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are often employed.
Detection Challenges: However, this is an ongoing arms race. As deepfake creation tools improve, they become better at hiding the signs detection tools look for. Furthermore, as highlighted by the Columbia Journalism Review, current detection tools often struggle with new deepfake methods, can be fooled intentionally, and their results can be hard to interpret (e.g., what does “30% artificial” mean?). Over-reliance on imperfect tools can create a false sense of security.
Legislation and Regulation: Governments worldwide are grappling with how to regulate deepfakes. Laws are being proposed or enacted to criminalize the creation and distribution of malicious deepfakes, particularly non-consensual explicit content and election interference. However, balancing free speech concerns with the need to prevent harm is challenging.
Platform Policies: Social media platforms and tech companies are implementing policies against harmful deepfakes and investing in detection technologies, but enforcement remains inconsistent and difficult at scale.
Watermarking and Authentication: Efforts are underway to develop ways to digitally “watermark” authentic media at the point of creation or use blockchain to verify content provenance, making it easier to distinguish real from fake.
Public Awareness and Media Literacy: Perhaps the most crucial defense is a critical public. We all need to develop better media literacy skills:
- Be Skeptical: Approach sensational or surprising online content with caution, especially if it evokes strong emotions.
- Check the Source: Who shared this? Is it a reputable source? Can you find the information confirmed elsewhere by trusted news outlets?
- Look Closely: Examine videos and images for the tell-tale signs mentioned earlier.
- Use Reverse Image Search: Tools like Google Image Search can sometimes help find the origin of an image or similar images.
- Establish Safe Practices: For personal security against voice scams, Security.org suggests setting up a secret code word with family members to verify identity during urgent requests for help.

The Future is Blurry: What’s Next for Deepfakes?

Deepfake technology is advancing at breakneck speed. Fakes will likely become even more realistic, easier to create, and harder to detect. We can expect to see:

Real-time Deepfakes: The ability to generate deepfakes instantly, potentially impacting live video calls or broadcasts.
More Sophisticated Audio Fakes: Voice cloning will likely become even more convincing and require less source audio.
Increased Use in Cybercrime: Fraudsters will continue to leverage deepfakes for more effective social engineering and identity theft.
Hyper-Personalized Misinformation: Tailoring fake content to individuals based on their online data to maximize impact.
Continued Arms Race: Detection methods will improve, but so will evasion techniques used by deepfake creators.

Navigating this future will require ongoing technological innovation, thoughtful regulation, platform responsibility, and, above all, a commitment from all of us to be more critical consumers of information.

Conclusion: Seeing Isn’t Always Believing Anymore

Deepfakes represent a profound shift in our relationship with digital media. Powered by sophisticated AI, this technology holds both creative promise and a terrifying potential for misuse. From undermining elections and perpetrating massive fraud to enabling vicious personal attacks and eroding the very foundation of trust in what we see and hear, the dangers are real and growing rapidly.