Deepfake Detection

Deepfake detection studies how to distinguish authentic images, video, and audio from manipulated or AI-generated content, a capability important for verification and security. Detectors look for spatial and frequency artifacts, temporal or physiological irregularities, and inconsistencies across audio, video, and semantic or physical cues; some also use pretrained foundation models, vision-language reasoning, or watermarks. Beyond classifying media, methods increasingly aim to localize altered regions or time spans, identify sources, and provide evidence-grounded explanations. A central challenge is that strong results on familiar benchmarks often fail to transfer to unseen generators, real-world conditions, or degraded media, while shortcuts, calibration, and fairness complicate reliable deployment.