Global Texture Sensitive Convolutional Transformer for Medical Image Steganalysis

Abstract

The use of medical images by hackers or illegal organizations as a vehicle for information leakage refers to steganography. 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' personal 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 location specificity of different parts in image and then extracts possible steganographic information by composing mutlihead 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 recent advanced steganography detectors.