Semi-Blind watermarking using convolutional attention-based turtle shell matrix for tamper detection and recovery of medical images

Abstract

“Telemedicine or e-medicine” refers to an electronic communication system that shares medical information from one location to another through the transmission channel. The increase in multimedia data sharing/transmission across the communication channel has raised to an uncontrollable stage. Thus, the transmission of medical reports across the network has increased higher, which has a chance of tampering either intentional or unintentional attacks. In the case of deliberate attacks, tamper detection and localization are the highly required criteria to verify the originality. In such cases, medical image authentication, security, and integrity verification are also essential criteria in telemedicine applications to verify whether the report belongs to the right person or not. To achieve these criteria and to satisfy these requirements Digital watermarking techniques are highly suggested. Much research has been carried out to address this issue, but some drawbacks and limitations still need to be addressed. The objective is to accomplish a Tamper detection, localization, and recovery (TDLR) system with minimal source information at the receiver end with a better payload or embedding capacity. This paper presents a novel Conventional Attention network (CoAtNet) based invariant watermark generation. A two-level embedding is proposed to enhance watermark security, where image tamper localization and recovery information is generated. The primary level of embedding is carried out by the Turtle Shell Data Hiding Algorithm (TSDH), and the secondary level of embedding is carried out by the DWT-SVD transform. The result shows that the proposed model achieves high imperceptibility and tamper detection accuracy of 60.24 and 99.51% respectively. Furthermore, the suggested system performed well in evaluations of tamper detection and image restoration for various malicious attacks against state-of-the-art systems.