Search

Information steganography has received more and more attention from scholars nowadays, especially in the area of image steganography, which uses image content to transmit information and makes the existence of secret information undetectable. To enhance concealment and security, the Steganography without Embedding (SWE) method has proven effective in avoiding image distortion resulting from cover modification. In this paper, a novel encrypted communication scheme for image SWE is proposed. It reconstructs the image into a multi-linked list structure consisting of numerous nodes, where each pixel is transformed into a single node with data and pointer domains. By employing a special addressing algorithm, the optimal linked list corresponding to the secret information can be identified. The receiver can restore the secret message from the received image using only the list header position information. The scheme is based on the concept of coverless steganography, eliminating the need for any modifications to the cover image. It boasts high concealment and security, along with a complete message restoration rate, making it resistant to steganalysis. Furthermore, this paper proposes linked-list construction schemes within the proposed framework, which can effectively resist a variety of attacks, including noise attacks and image compression, demonstrating a certain degree of robustness. To validate the proposed framework, practical tests and comparisons are conducted using multiple datasets. The results affirm the framework's commendable performance in terms of message reduction rate, hidden writing capacity, and robustness against diverse attacks. [ABSTRACT FROM AUTHOR]

The number of patients with cardiovascular diseases worldwide is increasing rapidly, while medical resources are increasingly scarce. Heart sound classification, as the most direct means of discovering cardiovascular diseases, is attracting the attention of researchers around the world. Although great progress has been made in heart sound classification in recent years, most of them are based on traditional statistical feature methods and temporal dimension features. These traditional temporal dimension feature representation and classification methods cannot achieve good classification accuracy. This paper proposes a new partition attention module and Fusionghost module, and the entire network framework is named PANet. Without segmentation of the heart sound signal, the heart sound signal is converted into a bispectrum and input into the proposed framework for feature extraction and classification tasks. The network makes full use of multi-scale feature extraction and feature map fusion, improving the network feature extraction ability. This paper conducts a comprehensive study of the performance of different network parameters and different module numbers, and compares the performance with the most advanced algorithms currently available. Experiments have shown that for two classification problems (normal or abnormal), the classification accuracy rate on the 2016 PhysioNet/CinC Challenge database reached 97.89%, the sensitivity was 96.96%, and the specificity was 98.85%. [ABSTRACT FROM AUTHOR]