Trusted Preservation and Traceability Mechanism of Electronic Evidence Chain for Forensic Medical Imaging

Abstract

Electronic evidence of forensic medical images plays a key role in forensic identification. The existing deposit technology is difficult to cope with the dual challenges of image format change and AI forgery, and the fusion mechanism of digital watermarking and blockchain has the problems of robustness and traceability accuracy imbalance. This article proposes a dynamic trusted certificate storage system that integrates deep learning perceptual hash and alliance chain. A semantic hash generation network based on multi-scale frequency domain features is designed, and a lightweight intelligent contract architecture optimized by SM2/SM3 algorithm of state secrets is established. The full link traceability is realized by combining adaptive frequency domain and time domain nested watermarking algorithms. Experiments show that under the attacks of Gaussian noise, JPEG compression and geometric deformation, the Hamming distance of the hash is stable within 3 bits, which is better than the mutation of more than 30 bits in the traditional cryptographic hash. When the rotation is 10, the false recognition rate is less than 1%, and the sample collision probability is maintained at a very low order of magnitude; When the watermark embedding strength increases, the PSNR remains above 45 dB, and the normalized cross-correlation coefficient is higher than 0.92 under the condition of JPEG compression quality of 70. In the alliance chain scenario, the consensus delay of 30 nodes is 180 ms, and the delay rises to 320ms after the expansion of 100 nodes, and the system throughput is not significantly attenuated. In this study, the synergy between robustness, transparency and traceability efficiency is optimized, which can provide a reference technical scheme for judicial acceptance of forensic electronic evidence chain.