  Due to its high societal impact, deepfake detection is getting active
attention in the computer vision community. Most deepfake detection methods
rely on identity, facial attributes, and adversarial perturbation-based
spatio-temporal modifications at the whole video or random locations while
keeping the meaning of the content intact. However, a sophisticated deepfake
may contain only a small segment of video/audio manipulation, through which the
meaning of the content can be, for example, completely inverted from a
sentiment perspective. We introduce a content-driven audio-visual deepfake
dataset, termed Localized Audio Visual DeepFake (LAV-DF), explicitly designed
for the task of learning temporal forgery localization. Specifically, the
content-driven audio-visual manipulations are performed strategically to change
the sentiment polarity of the whole video. Our baseline method for benchmarking
the proposed dataset is a 3DCNN model, termed as Boundary Aware Temporal
Forgery Detection (BA-TFD), which is guided via contrastive, boundary matching,
and frame classification loss functions. Our extensive quantitative and
qualitative analysis demonstrates the proposed method's strong performance for
temporal forgery localization and deepfake detection tasks.
