Efficient Lightweight Multiscale Attention Network for Image Steganalysis

With the combination of deep learning, steganography is becoming more and more resistant to steganalysis, making it more and more difficult for traditional steganalysis to extract features from steganographic images. In this paper, we address the above problems and propose an efficient lightweight image steganalysis method (ELMANet) based on cascaded multiscale neural networks and the multiscale hybrid attention mechanism to extract more significant features from steganographic images. The architecture of ELMANet comprises three integral components: a preprocessing module, a feature extraction module, and a classification module. Through rigorous experimental validation on different datasets, ELMANet performs better in terms of steganalysis detection accuracy, while requiring lower network parameters and computational resource overheads compared to existing image steganalysis models.