计算机科学
数字水印
稳健性(进化)
水印
加密
混乱的
人工神经网络
计算机安全
人工智能
互联网
人为噪声
数据挖掘
计算机工程
计算机网络
嵌入
图像(数学)
发射机
万维网
生物化学
化学
频道(广播)
基因
作者
Xiuli Chai,Zongwei Tang,Zhihua Gan,Yang Lu,Sheng Wang,Yushu Zhang
标识
DOI:10.1016/j.bspc.2023.105877
摘要
The development of the Internet of Medical Things heavily relies on big data, and data security based on medical images has become a growing concern in society. Digital watermarking serves as a crucial technique for protecting and tracing medical image data copyright, as well as enabling forensic analysis. However, existing deep watermarking methods often neglect the protection of watermarks after extraction, leading to potential copyright disputes. To address this issue, this paper proposes SE-NDEND, a novel symmetric watermarking framework with neural network-based chaotic encryption for the Internet of Medical Things that significantly enhances the effectiveness and security of watermarking while maintaining robustness. Specifically, the SE-NDEND leverages neural networks to simulate chaotic systems and generate chaotic sequences, mitigating the complexity and high cost of implementing chaotic systems using hardware circuits. Moreover, we introduce a new noise layer with Moiré distortion that interacts with the decoder, forming a symmetric network structure that bolsters the robustness of watermarking. Parameters are jointly trained and shared during the training process to counteract potential interference from the noise layer. Experimental results validate the effectiveness of SE-NDEND in enhancing copyright protection, traceability, and forensic capabilities, surpassing existing deep learning methods in terms of visual quality (with PSNR of 45.8492 dB and SSIM of 0.9874), security, and robustness. The proposed framework can find application in protecting medical image data in the Internet of Medical Things.
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