Video Content Plagiarism Detection Using Region-Based Feature Learning

Due to the continuous increase in copyright infringement cases of video content, the economic losses of copyright owners continue to rise. To improve the efficiency of plagiarism detection in video content, in this paper, we propose region-based video feature learning. The first innovation of this paper lies in the combination of temporal positional encoding and attention mechanisms to extract global features for weakly supervised model training. Self- and cross-attention mechanisms are combined to enhance similar features within and between videos by incorporating position coding to capture timing relationships between video frames. Global classification description is embedded for capturing global spatiotemporal information and combined with a weak supervised loss for model training. The second innovation is the frame sequence similarity calculation, which is composed of Chamfer similarity, coordinate attention mechanism, and residual connection, to aggregate similarity scores between videos. Experimental results show that the proposed method can achieve the mAP of 0.907 on the short video dataset from Douyin. The proposed method outperforms frame-level and video-level features in achieving higher detection accuracy, and further contributes to the improvement of video content plagiarism detection performance.