Steganalysis of Image with Adaptively Parametric Activation
This work addresses the challenge of detecting steganography in images, which is crucial for security applications, but it appears incremental as it builds on existing methods with specific improvements.
The paper tackled the problem of detecting hidden messages in images (steganalysis) by enhancing weak embedding signals through adaptive parametric activation, constrained high-pass filters, and a contrastive loss function, achieving competitive performance compared to state-of-the-art methods on BOSSbase 1.01 images with WOW and S-UNIWARD stegos.
Steganalysis as a method to detect whether image contains se-cret message, is a crucial study avoiding the imperils from abus-ing steganography. The point of steganalysis is to detect the weak embedding signals which is hardly learned by convolution-al layer and easily suppressed. In this paper, to enhance embed-ding signals, we study the insufficiencies of activation function, filters and loss function from the aspects of reduce embedding signal loss and enhance embedding signal capture ability. Adap-tive Parametric Activation Module is designed to reserve nega-tive embedding signal. For embedding signal capture ability enhancement, we add constraints on the high-pass filters to im-prove residual diversity which enables the filters extracts rich embedding signals. Besides, a loss function based on contrastive learning is applied to overcome the limitations of cross-entropy loss by maximum inter-class distance. It helps the network make a distinction between embedding signals and semantic edges. We use images from BOSSbase 1.01 and make stegos by WOW and S-UNIWARD for experiments. Compared to state-of-the-art methods, our method has a competitive performance.