The boom in telemedicine and digital healthcare has spurred a surge in demand for medical image transmission, especially in remote areas with limited bandwidth, imposing a heavy burden on communication systems. To address the challenge of efficient transmission of massive medical images, this paper proposes a semantic communication-based solution called medical image joint source channel coding (Med-JSCC). Our motivation stems from the fact that during clinical diagnosis, medical professionals predominantly focus on regions of interest (ROI), i.e., critical regions, while paying relatively less attention to non-region of interest (NROI). This inspires us to adopt a differentiated processing strategy. Specifically, we first design a mask-guided feature processing module, where the mask generated by the large medical image segmentation model (e.g., MedSAM-2) identifies ROI-relevant and ROI-irrelevant semantic features. On this basis, a differentiated processing strategy is proposed to balance transmission efficiency and diagnostic reliability. Furthermore, the proposed Med-JSCC integrates an adaptive transmission module, including variable-length coding and a channel adaptive unit (CAU). The former can assign transmission rates to semantic features based on a learned entropy model, while the latter improves the robustness against channel variations by recalibrating semantic features based on channel parameters. Experimental results on dental and chest X-ray datasets demonstrate that our method effectively improves transmission efficiency while preserving diagnostically critical information in medical images.

Static deep neural network (DNN) watermarking techniques typically employ irreversible methods to embed watermarks into the DNN model weights. However, this approach causes permanent damage to the watermarked model and fails to meet the requirements for integrity authentication. Reversible data hiding (RDH) methods offer a potential solution, but existing approaches suffer from limitations in usability, capacity, and fidelity, hindering their practical adoption. In this paper, we propose a secure static DNN watermarking scheme called Secure Difference Contraction (SDC). Our scheme utilizes a one-dimensional quantizer for watermark embedding and employs dithering to ensure key-dependent security, i.e., the watermark cannot be correctly extracted without the secret key used during embedding. Additionally, we design two schemes to address the challenges of integrity protection and legitimate authentication for DNNs. Simulation results on training loss and classification accuracy demonstrate the feasibility and effectiveness of our proposed methods, highlighting their advantages in capacity and fidelity over existing techniques.

This paper delves into the challenges of spread spectrum (SS) watermarking extraction, considering both reference-free and referential extraction scenarios, within the framework of lattice decoding. The orthogonality of carriers plays a crucial role in the accuracy of extraction, impacting the bit error rate (BER). When carriers lack sufficient orthogonality, conventional reference-free extraction methods such as multi-carrier iterative generalized least-squares (M-IGLS) and referential extraction techniques like MMSE-based schemes encounter performance degradation, posing difficulties in accurately recovering hidden data at the receiver end. To address these challenges, we propose two novel SS watermarking extraction approaches by integrating precise lattice decoding algorithms. Firstly, we introduce the highly accurate yet computationally efficient successive interference cancellation (SIC) algorithm to augment M-IGLS, resulting in a new method termed multi-carrier iterative successive interference cancellation (M-ISIC). Secondly, we adapt the near-optimal sphere decoding (SD) technique for referential extraction in SS watermarking. Theoretical analysis and experimental simulations showcase that our proposed M-ISIC and SD methods outperform M-IGLS and MMSE-based detectors, particularly in scenarios where carrier orthogonality is limited, achieving lower BER. Our code is available athttps://github.com/shx-lyu/M_ISIC.