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Deepfake

Deepfake technology is the weaponization of generative AI. It is the end of the era where “seeing is believing.” A deepfake is not just a manipulated video or a “photoshop”; it is a synthetic creation, an AI-generated forgery of reality that can be used to create false evidence, destroy reputations, and manipulate markets. From a legal standpoint, it is a crisis for the entire system of evidence.

Analogy: The Evolution of Forgery

Think of the legal history of forgery.

  1. The Crude Forgery: For centuries, a skilled artist could try to forge a signature on a document. An expert could look at the ink, the pressure of the pen strokes, and the paper to determine it was fake.
  2. The Digital Forgery: With the advent of Photoshop, a forger could digitally copy-paste a real signature onto a fake document. A forensic analyst could look at the pixel level, find compression artifacts, and expose the manipulation.
  3. The AI Forgery (Deepfake): Now, an AI can be trained on thousands of images and audio samples of a person. It doesn’t copy-paste anything. It learns the pattern of that person’s likeness—how they move, talk, and express emotion. Then, it generates an entirely new, synthetic video of that person saying or doing anything the attacker wants.

There is no original signature to compare against. There are no obvious copy-paste artifacts. The AI has generated a new reality from scratch. This is a paradigm shift in the nature of forgery.

The rise of deepfakes creates two urgent problems for the legal profession.

  1. The Liar’s Dividend: This is the most insidious effect. It’s not just that people will believe fake videos; it’s that people will be able to dismiss real videos as fakes. If an authentic, damning video of a CEO or politician emerges, they can simply claim, “That’s not me, it’s a deepfake.” In a world where perfect fakes are possible, it becomes much harder to prove that a real video is, in fact, real. This benefits liars and undermines accountability.

  2. The Arms Race in Forensics: Deepfake detection is a cat-and-mouse game. The AI models used to create deepfakes and the AI models used to detect them are trained in an adversarial process. As soon as a detector learns to spot a certain flaw (e.g., weird blinking), the next generation of deepfake models learns to fix that flaw. There is no permanent “tell.” Any claim that a piece of software can “reliably detect deepfakes” should be treated with extreme skepticism. The forgers are almost always one step ahead of the forensic analysts.

For litigators, this means the chain of custody and the provenance of digital evidence are now more important than ever. Where did the video come from? Who had access to it? Is there a cryptographically secure “digital watermark” from the recording device? Without a verifiable path from the camera to the courtroom, any piece of digital media is vulnerable to the claim that it’s a deepfake, whether it is or not.