Global texture sensitive convolutional transformer for medical image steganalysis

Global texture sensitive convolutional transformer for medical image steganalysis

연구 분야: Strategies

학회: Multimedia Systems

Steganography is often used by hackers or illegal organizations as a vehicle for information interception of medical images. Exchanged between PACS or communicated during telemedicine sessions, images are modified to hide data. Such leaks through stego-images may result in the disclosure of doctors’ or patients’ data, or of sensitive hospital data posing thus major risks in terms of privacy and security of the information system. In this paper, to detect these illegal image-based communications, we propose a steganalysis approach, the originality of which relies on a novel neural network GTSCT-Net. This one first extracts texture features as global texture features based on the location specificity of different image parts and then extract possible steganographic information by composing multihead self-attention and deep convolution blocks. It also offers easier convergence and higher accuracy on a lower information embedding rate. Comparative experiments on private and public datasets show that the performance of GTSCT-Net for medical image intrusion detection is separately up to 10.12% and 2.97% better than recently advanced steganography detectors.