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Due to its high societal impact, deepfake detection is getting activeattention in the computer vision community. Most deepfake detection methodsrely on identity, facial attributes, and adversarial perturbation-basedspatio-temporal modifications at the whole video or random locations whilekeeping the meaning of the content intact. However, a sophisticated deepfakemay contain only a small segment of video/audio manipulation, through which themeaning of the content can be, for example, completely inverted from asentiment perspective. We introduce a content-driven audio-visual deepfakedataset, termed Localized Audio Visual DeepFake (LAV-DF), explicitly designedfor the task of learning temporal forgery localization. Specifically, thecontent-driven audio-visual manipulations are performed strategically to changethe sentiment polarity of the whole video. Our baseline method for benchmarkingthe proposed dataset is a 3DCNN model, termed as Boundary Aware TemporalForgery Detection (BA-TFD), which is guided via contrastive, boundary matching,and frame classification loss functions. Our extensive quantitative andqualitative analysis demonstrates the proposed method's strong performance fortemporal forgery localization and deepfake detection tasks.

Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization