Title: Infringing Video Retrieval via Decoupled Representations and Mutual Information Maximization. Abstract: Video copyright infringements is a major issue faced by the self-media industry and video sharing platforms, causing serious damages to the interests and enthusiasm of video creators. Identifying such infringing videos is therefore a compelling task. Current state-of-the-art methods typically build on frame-level feature representations extract via pre-trained deep neural networks. Despite its simplicity, this paradigm heavily relies on the original feature representation and lacks an effective constraint on guaranteeing that useful task-relevant semantics are learned from the feature. In this paper, we seek to tackle these challenges from two aspects: (1) We propose to disentangle the high-dimensional original feature Infringing Video Retrieval via Decoupled Representations and Mutual Information Maximization into multiple sub-features, explicitly decoupling the feature into exclusive lower-dimensional components. We expect the sub-features to encode non-overlapping semantics of the original feature and reduce task-irrelevant redundancy. (2) Besides the disentangled sub-features, we further learn a supplementary feature, and formulate a mutual information objective to enforce sufficiency of task-relevant information. Extensive experiments on two large-scale benchmark datasets SVD and VCSL demonstrate that our method achieves 88.87% and 94.46% TOP-100 accuracies respectively, significantly surpassing state-of-the-arts. Interestingly, visualization of the extracted features reveals that our approach indeed extracts disentangled sub-features and holistic global feature. Our code and model are uploaded in the supplementary file and anonymously released at https://github.com/iclranonymous/DMI.