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Videos are prone to tampering attacks that alter the meaning and deceive theaudience. Previous video forgery detection schemes find tiny clues to locatethe tampered areas. However, attackers can successfully evade supervision bydestroying such clues using video compression or blurring. This paper proposesa video watermarking network for tampering localization. We jointly train a3D-UNet-based watermark embedding network and a decoder that predicts thetampering mask. The perturbation made by watermark embedding is close toimperceptible. Considering that there is no off-the-shelf differentiable videocodec simulator, we propose to mimic video compression by ensembling simulationresults of other typical attacks, e.g., JPEG compression and blurring, as anapproximation. Experimental results demonstrate that our method generateswatermarked videos with good imperceptibility and robustly and accuratelylocates tampered areas within the attacked version.

Robust Watermarking for Video Forgery Detection with Improved Imperceptibility and Robustness