Abstract: This paper proposes a watermark recovery algorithm for medical images against JPEG compression to address the problems of privacy protection and transmission efficiency of medical images in telemedicine.Traditional methods such as parity-check codes and Hemming codes have limitations in watermark error correction,while Reed-Solomon codes can effectively recover multi-bit error,but their recovery ability is limited when facing block processing attacks such as JPEG compression.With the development of deep learning,although INN-based watermarking technology realizes high-capacity information embedding,the computational burden is large,which affects the efficiency of information transfer.To solve these problems,this paper firstly applies Reed-Solomon code to preprocess the watermark information to improve its stability and recovery ability,and embeds the processed watermark into the DCT low-frequency coefficients of the carrier image.Secondly,in order to reduce the computation time,this paper is inspired by the structure of CSPNet,divides the features into two parts,optimizes the network structure of INN through cross-stage connection,reduces the number of model parameter,and accelerates the training process.The experimental results show that the algorithm achieves nearly 100% correct watermark recovery rate under JPEG compression with QF=50,and reduces the training time by about 40%,which significantly improves the computational efficiency and training speed of the proposed model.

Key words: Medical images, Privacy protection, Anti-JPEG compression, CSPNet, INN