Deepfake

Deepfakes are AI-generated synthetic media — most commonly videos — that depict real people appearing to say or do things they never actually did. The term combines 'deep learning' with 'fake.' The technology typically uses Generative Adversarial Networks (GANs), autoencoders, or diffusion models to learn a person's facial features, expressions, and voice from existing footage, then synthesize new content that is increasingly difficult to distinguish from authentic video. Audio deepfakes can clone a person's voice from just a few seconds of sample audio.

The democratization of deepfake technology — through open-source tools, mobile apps, and cloud services — has made creation accessible to anyone with basic technical skills. While there are legitimate applications in film production, entertainment, and accessibility (such as giving voice to those who have lost it), the risks are severe: political disinformation campaigns using fabricated speeches, financial fraud using cloned voices to authorize transactions, non-consensual intimate imagery, and erosion of public trust in authentic video evidence.

Detection and defense against deepfakes is an active arms race. Detection methods analyze subtle artifacts — unnatural blinking patterns, inconsistent lighting, audio-visual synchronization mismatches — but as generation technology improves, these artifacts become harder to find. Watermarking, provenance tracking (C2PA/Content Credentials), and cryptographic authentication of original media offer more sustainable defense strategies. Legal frameworks are evolving, with many jurisdictions introducing legislation specifically targeting malicious deepfake creation and distribution.