Self-Information Forgery Mining for Face Forgery Detection

In the face of rapid advances in face forgery technology, effective detection methods have become crucial to maintain the authenticity of digital media. Deep learning technology has provided new strategies for recognizing and preventing face forgery in recent years. In this study, a new face forgery detection technique is proposed by utilizing self-information theory, which improves the accuracy and robustness of detection by mining forgery traces, especially in diverse forgery scenarios. The study extracts face features through an improved high-resolution network HRNet and optimizes identity information extraction by combining facial reenactment techniques to detect forged faces efficiently. Experiments have been conducted on several mainstream forged face datasets, and the method presented in this paper can effectively improve the detection performance with an average accuracy of 74.75% on C40 recompressed images. Comparison experiments show that this research method’s frame-level and video-level detection accuracy on the Celeb-DF dataset are 0.9846 and 0.9985, respectively, which are higher than those of existing techniques. Cross-library tests validate the method’s generalization performance, and the AUC metric remains at 0.7305 even in low-quality video environments, which shows good resistance to environmental interference. This study proposes a self-information forgery mining technique that enhances forgery detection accuracy while demonstrating superior generalization ability.