Multi-Dimensional Automatic Detection Scheme for Abnormal Video Tampering Based on Digital Watermarking and CNN

This paper proposes an automatic detection scheme for abnormal video tampering that combines active and passive reference video forensics methods, aiming to cope with the increasingly complex problem of digital video tampering identification. The scheme combines digital watermarking embedding technology, convolutional neural network (CNN), and multiple traditional similarity evaluation metrics to build a multidimensional similarity evaluation system. Through modules such as video stream preprocessing, digital watermarking embedding, similarity metrics calculation, and automatic detection of abnormal tampering, it can achieve efficient detection of malicious tampering. Experimental results show that the detection accuracy of the scheme can reach 87 %, and its ability to detect minor tampering can surpass that of many existing solutions. It is highly sensitive to minor tampering, providing strong technical support for video copyright protection and video forensics.