"Deepfake detection is structurally reactive — detectors are…" — Jasmin P. Bharadiya (#c399)

Card #c399IR // #c399SynthesisThesis

Deepfake detection is structurally reactive — detectors are trained on outdated generation tech; the thesis measures EfficientNet-B4/B7 in two phases (untrained on the latest deepfake datasets vs. trained on them, both benchmark-validated) to quantify how much latest-generation training data improves detection effectiveness.

— In our words · Source: Effectiveness of EfficientNet-B4 and EfficientNet-B7 Deepfake Detection Techniques (Jasmin P. Bharadiya)

[Use when]

A detector trained on yesterday's threat is a history lesson, not a shield — detection systems must be retrained on the newest generation of fakes or they measure the past.

[Why it matters]

Deepfakes can fabricate a candidate saying things they never said; if detectors only reliably catch last year's fakes, the defense is permanently one generation behind the offense.