ASIGM: An Innovative Adversarial Stego Image Generation Method for Fooling Convolutional Neural Network-Based Image Steganalysis Models
Minji Kim, Youngho Cho, Hweerang Park, Gang Qu · Electronics · 2025
To defeat AI-based steganalysis systems, various techniques using adversarial example attack methods have been reported. In these techniques, adversarial stego images are generated using adversarial attack algorithms and steganography embedding algorithms sequentially and independently. However, this approach can be inefficient because both algorithms independently insert perturbations into a cover image, and the steganography embedding algorithm could significantly lower the undetectability or indistinguishability of adversarial attacks. To address this issue, we propose an innovative adversarial stego image generation method (ASIGM) that fully integrates the two separate algorithms by using the Jacobian-based Saliency Map Attack (JSMA). JSMA, one of the representative l0 norm-based adversarial example attack methods, is used to compute a set of pixels in the cover image that increases the probability of being classified as the non-stego class by the steganalysis model. The reason for this calculation is that if a secret message is inserted into the limited set of pixels in such a way, noise is only required for message embedding, and even misclassification of the target steganalysis model can be achieved without additional noise insertion. The experimental results demonstrate that our proposed ASIGM outperforms two representative steganography methods (WOW and ADS-WOW).