What are Deepfake? How AI-Generated Video and Media Work

What Is a Deepfake? How AI-Generated Video and Media Work

A deepfake is a piece of synthetic media, most often a deepfake video, image, or audio clip, that has been generated or altered by artificial intelligence to make a real person appear to say or do something they never did. The term blends “deep learning” with “fake”, and deepfake AI has advanced so quickly that a convincing forgery can now be built from only a small sample of genuine footage or a few seconds of recorded speech.

How Does a Deepfake Actually Work?

At a technical level, a deepfake is the output of a machine-learning model trained on real examples of a person, photos, video frames, or audio recordings, until it learns the patterns that make that person recognisable: the geometry of their face, the way they move, the tone and rhythm of their voice. Once trained, the model can generate new material on demand, placing the person into a scene, a sentence, or an audio clip they never actually appeared in. The output is not edited from real footage the way a photo might be cropped or retouched; it is generated from scratch by the model, the same quality exploited in deepfake presentation and injection attacks, and why a well-made deepfake can hold up under scrutiny that a manual edit could not.

Deepfake Video, Image, and Audio

Deepfakes appear in three broad forms:

• Deepfake video: a real person’s face or body is swapped into footage, or their expressions are puppeteered to match a script they never performed.
• Deepfake images: a still photo of a face, a document, or a scene is fabricated or altered to appear real.
• Deepfake audio: a voice is cloned from a short sample and made to say any text, a technique widely used in phone and social-engineering scams.

A Brief History of Deepfake Technology

The technology behind deepfakes did not appear overnight. The core technique, the generative adversarial network, was introduced in 2014 by machine learning researcher Ian Goodfellow, then a doctoral student, as a way to pit two neural networks against each other until one produced convincing fake images. The term “deepfake” itself only entered public use in December 2017, when an anonymous Reddit user posting under that name began sharing AI-manipulated videos, giving the technique both its name and its early notoriety. Within a few years, research that once required a dedicated lab had been packaged into consumer apps, turning a niche academic exercise into a technology available to anyone with a smartphone.

How Deepfake AI Works: GANs and Diffusion Models?

Deepfake AI is powered mainly by two families of models:

• Generative Adversarial Networks (GANs): two neural networks compete. A generator creates fakes while a discriminator tries to catch them. As they compete, the generator improves until its output can fool both the discriminator and, often, a human viewer.
• Diffusion models: the technology behind many of today’s most realistic image and video generators. They learn to reverse a process of adding noise, so they can start from random noise and gradually refine it into a photorealistic result.

Both approaches have lowered the barrier to entry. What once required large datasets and technical skill can now be done with consumer apps, sometimes in real time during a live video call.

This shift has been accelerated by generative AI deepfake tools that can produce synthetic video and audio in seconds.

The Different Types of Deepfakes

• Face swap: one person’s face is mapped onto another person’s head in a video.
• Face reenactment: the target’s own face is kept, but their expressions and head movements are driven by an actor or script.
• Lip-sync deepfakes: existing footage is altered so the mouth matches new audio.
• Voice cloning: a synthetic copy of a real voice, created from only seconds of speech.
• Full-body and text-to-video synthesis: entire people and scenes generated from scratch, increasingly from a simple text prompt.

Deepfakes vs Cheapfakes: Why the Distinction Matters?

Not every manipulated video is a deepfake. Researchers use the term “cheap fake”, sometimes called a “shallowfake”, for media that is altered using simple, low-cost methods rather than machine learning: splicing footage out of context, slowing or speeding up a clip, mislabeling a genuine video, or using basic editing software to swap a face. Cheap fakes require no AI at all, which makes them far easier to produce and, in practice, just as capable of misleading an audience. The distinction matters for verification: a cheap fake can often be caught by checking the original source or context, while a genuine deepfake requires the kind of technical analysis this guide describes. Treating every suspicious video as a potential deepfake risks missing the simpler, more common trick behind it.

The Evolution of Deepfake Technology

Early deepfakes needed hours of processing and still looked flawed. The technology has since shifted in three ways: models need far less source material, generation is fast enough to run live, and ready-made tools have spread widely. A threat once limited to well-resourced actors is now within reach of almost anyone, which is why deepfake fraud has moved from a novelty to a mainstream business risk.

This shift is visible in the rise of deepfake as a service offerings, which let anyone rent deepfake capabilities on demand.

What Are the Risks of Deepfakes?

The risks of deepfakes go far beyond a single fake video. They span financial, reputational, and societal harm.

Identity Fraud and Impersonation

• Synthetic faces and cloned voices are used to impersonate customers, bypass remote onboarding, defeat age verification checks, and open accounts under stolen or invented identities.

Financial and Reputational Damage

• Beyond direct theft, businesses face chargebacks, remediation costs, regulatory penalties under evolving deepfake laws, and lost customer trust that took years to build.

Misinformation and Manipulation

• Fabricated videos of public figures can spread false statements, and even genuine footage can be dismissed as fake, an effect that steadily erodes public trust.

How Are Deepfakes Used in Online Scams?

Deepfakes are used in scams to impersonate someone the victim already trusts, which lowers their guard. Common tactics include:

• CEO and executive fraud: A cloned voice or video of a senior leader authorising an urgent payment.
• Family and friend impersonation: Cloned voices in distress calls demanding money.
• Romance and investment scams: Synthetic personas built to gain trust over time.
• Fake endorsements: Deepfaked celebrities or officials promoting crypto and trading schemes.
• Verification bypass: Fake selfies and videos used to defeat remote identity checks.

The scale is real. In 2024, a finance employee at engineering firm Arup was deceived into transferring around US$25 million after joining a video call populated entirely by deepfake recreations of senior colleagues.

Which Industries Are Most Affected by Deepfakes?

Any sector that relies on trusting a person’s identity or image is exposed, but some are targeted more heavily than others:

• Financial services and banking: Account opening, payment authorisation, and high-value transfers.
• Fintech and cryptocurrency: Fast, remote onboarding and irreversible transactions.
• Insurance: Fraudulent claims supported by manipulated images and video.
• Government and public services: Benefits fraud and identity misuse.
• Media and entertainment: Reputational attacks and fabricated statements.
• Telecommunications: SIM-swap and account-takeover support scams.
• Gig and sharing economy: Fake driver, host, and worker identities.

Remote KYC and onboarding checks are the common thread across every sector on this list.

What Are the Most Common Signs of a Deepfake?

While the best forgeries are hard to catch by eye, many deepfakes still leave clues. Knowing the common signs helps people and teams stay alert.

Visual signs in video and images

• Unnatural blinking, or eyes that do not track or reflect light consistently.
• Blurring or warping where the face meets hair, ears, glasses, or the neckline.
• Skin that looks too smooth, waxy, or mismatched with the lighting.
• Lip movements slightly out of sync, or flat, undefined teeth.
• Flickering edges and shadows that shift oddly between frames.

Audio signs

• A flat or robotic cadence with unusual pauses or emphasis.
• Background noise that cuts in and out unnaturally.
• Missing or misplaced breathing and mouth sounds.

Contextual signs

• Urgency, secrecy, or pressure to act quickly.
• Unusual requests, especially involving money or credentials.
• A channel or behaviour that does not match how the person normally communicates.

These manual checks help individuals, but they do not scale. A business verifying thousands of people cannot eyeball every selfie or video for warping and flicker. Organisations that need to catch synthetic media automatically and at scale rely on purpose-built liveness detection and deepfake detection as part of their identity verification process.

Will Deepfakes Become Impossible to Distinguish from Real Content?

Deepfakes are already convincing enough to fool the human eye, and quality will keep improving as generation becomes cheaper and faster. But they are unlikely to become truly impossible to distinguish. Provenance standards such as content credentials, digital watermarking, and layered verification are advancing in parallel. The realistic future is a continuing contest in which authenticity is verified rather than assumed, and organisations that treat every identity as something to confirm will be best placed to cope.

How Shufti Help Businesses Stay Ahead?

Shufti provides identity verification and fraud prevention for businesses in 240+ countries and territories. By combining biometric verification, liveness checks, and AI-driven analysis of synthetic media, Shufti helps organisations confirm that the person behind a screen is genuine, present, and who they claim to be, so fabricated faces and cloned voices are caught before they cause harm. To see how this works in practice, explore Shufti’s approach to deepfake detection, which combines liveness checks, document forensics, and behavioural signals in a single verification flow.