Deepfakes in the Foundation-Model Era

Deepfake detection is usually measured on curated benchmarks, yet synthetic video often reaches audiences through social-media platforms that compress, resize, re-encode, crop, and repost it before downstream detection. The practical question is therefore not whether a detector separates real from fake under laboratory conditions, but whether the evidence it depends on still exists at the moment a decision must be made. This survey reviews 212 works on generative audio-video deepfakes across the full lifecycle of creation, platform distribution, detection, provenance, and remediation. We organize the review around five forensic assumptions that detectors implicitly rely on: that a manipulated region leaves a boundary, that a generator leaves a stable fingerprint, that test data resemble training data, that forensic signals survive processing, and that low-level clues are sufficient. Using these assumptions as a common lens, we present an evidence-based taxonomy of generation methods, review signal-driven, learning-based, reasoning-based, agentic, and adversarially robust detection, and examine provenance mechanisms including content credentials and watermarking alongside emerging disclosure and takedown regulation. Two findings recur. First, foundation-model generation can weaken several assumptions at once, since fully synthetic and jointly generated audio-video content may remove compositing boundaries and reduce exploitable cross-modal inconsistencies. Second, the literature is concentrated on creation and detection, with distribution, provenance, and remediation together accounting for 13% of the corpus. We close with evaluation protocols that report which assumptions a benchmark actually exercises.