Robust Video Hashing Based on Local Fluctuation Preserving for Tracking Deep Fake Videos

With the rapid development of deepfake techniques, massive face manipulation videos appeared on social networks. These deepfake videos not only violated the original video of the author’s privacy, but also seriously threatened the security of the video database. Robust video hashing can map videos with similar visual content into similar hash codes, which is beneficial for tracking fake video material in social networks. In this paper, a robust video hashing algorithm based on local fluctuation preserving is proposed. The algorithm uses a shot segmentation model and local statistical descriptors, which is robust to many commonly-used digital operations and can accurately track the original version of these fake videos from a huge video database. An essential contribution is a shot segmentation model reconstruction from input video with image hashing and discrete wavelet transform, reaching initial data compression and against noise attack. In addition, as local statistical descriptors are content-based and local preserving features, the hash generated by local statistical descriptors can achieve good discrimination and ensure that the proposed hash has good tracking ability to fake videos from original videos.