Beyond traditional steganography: enhancing security and performance with spread spectrum image steganography

This study investigates the innovative application of Direct Sequence Spread Spectrum (DSSS) technology in the realm of image steganography, known as Spread Spectrum Image Steganography (SSIS). By interpreting the cover image as noise in the communication channel, SSIS capitalizes on the noise-resistant properties of broadband communication systems to effectively conceal information within images. We focus on the development of new classes of spreading sequences with desirable ensemble and correlation properties, which significantly impact the performance of SSIS. We propose a data hiding method that directly addresses spreading sequences, resulting in minimized cover image distortion and heightened resistance to message detection. Furthermore, we explore adaptive spreading sequences that consider the statistical properties of the cover image, substantially reducing error intensity in recovered messages and improving the overall steganographic system performance. Our experiments confirm the advantages of the proposed system and support the theoretical arguments. In addition, we employ artificial neural networks for steganalysis, generating several datasets with varying SSIS payloads and examining the detectability of embedded data using a specially designed convolutional neural network (CNN). While this model demonstrates high effectiveness on other datasets, the detection error probability for SSIS is considerably higher, indicating greater reliability and security even when advanced steganalysis techniques are employed. The findings highlight the potential of SSIS in developing robust and secure communication systems capable of functioning effectively in high-noise environments while preserving the integrity of the cover image.