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"Glitch in the Matrix!": A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization. (arXiv:2305.01979v2 [cs.CV] UPDATED)
cs.CV updates on arXiv.org arxiv.org
Most deepfake detection methods focus on detecting spatial and/or
spatio-temporal changes in facial attributes. This is because available
benchmark datasets contain mostly visual-only modifications. However, a
sophisticated deepfake may include small segments of audio or audio-visual
manipulations that can completely change the meaning of the content. To
addresses this gap, we propose and benchmark a new dataset, Localized Audio
Visual DeepFake (LAV-DF), consisting of strategic content-driven audio, visual
and audio-visual manipulations. The proposed baseline method, Boundary Aware
Temporal Forgery Detection …
arxiv audio benchmark change datasets deepfake detection focus glitch localization matrix scale small temporal the matrix