Robust zero-watermarking scheme based on a depthwise overparameterized VGG network in healthcare information security. (March 2023) - Huang, Tongyuan
Abstract: As healthcare information technology has rapidly evolved, securely storing and transmitting medical data online and successfully protecting patient privacy are currently the research focus in the healthcare information field. To better protect the security of medical data, this paper introduces deep neural network and convolutional block attention module (CBAM) into the study of watermarking techniques and proposes a medical image zero-watermarking scheme based on depthwise overparameterized VGG (DO-VGG). First, we extract the high-dimensional abstract feature information of medical images using the pretrained DO-VGG model. Then, the construction of the zero-watermarking scheme utilizes the mean-perceptual hashing algorithm, which can efficiently resist both common and geometric attacks. Meanwhile, using the improved logistic mapping to encrypt the watermarking image effectively improves the security of the scheme. Experimental results indicate that all NC values of the proposed scheme are maintained above 0.8 under various degrees of attacks, which has good robustness and invisibility. The proposed scheme can satisfy the special requirements of medical image integrity and effectively protect the private information of medical images. Highlights: The proposed zero-watermarking scheme is based on depthwise overparameterized VGG. The DO-Conv speeds up convergence and improves the performance of the network. The proposed scheme combines the CBAM to effectively improve robustness to various attacks. The improved logistic chaotic system effectively improved the scheme's security.