Explainable Deepfake Detection

Explainable deepfake detection studies how to identify manipulated images, videos, and audio while making the evidence behind a prediction understandable and checkable. Approaches include highlighting suspicious regions or time segments, using explicit forensic and phonetic features, and training vision-language models to connect explanations to localized evidence. Evaluation uses datasets and benchmarks for detection, localization, explanation quality, and cross-dataset testing across generators, recording conditions, and media types. A central challenge is ensuring that convincing explanations reflect cues that actually drive the decision, while remaining reliable on unfamiliar manipulations and after compression or other changes.