Image Steganalysis Against Adversarial Steganography by Combining Confidence and Pixel Artifacts
Mingzhi Hu, Hongxia Wang
Abstract
Convolutional Neural Networks (CNNs) have made remarkable progress in steganalysis. However, they struggle to detect adversarial steganography accurately which merges adversarial samples and steganography. While handcrafted models show limited vulnerability to adversarial steganography, their accuracy pales in comparison to that of CNN analyzers. To address these limitations head-on, we propose TStegNet, an innovative two-stream CNN steganalyzer designed to detect adversarial steganography. TStegNet leverages confidence artifacts and pixel artifacts, enabling a comprehensive analysis of hidden information. Specifically, we design a confidence loss function and apply backpropagation to amplify the confidence artifacts, which enhances the performance of our model. Additionally, we use the feature similarity function to minimize the impact of adversarial perturbation. Extensive experiments reveal that proposed TStegNet outperforms existing state-of-the-art methods, representing a significant milestone in the fight against adversarial steganography.