Adversarial Steganography Embedding via Stego Generation and Selection

Abstract

The recent literature has shown that adversarial embedding has promise for enhancing the security of steganography. However, existing methods achieve the final stego mainly based on a pre-trained Convolutional Neural Network (CNN)-based steganalyzer without considering any other steganalytic features. When the steganalyzer is re-trained, its performance usually drops significantly. We propose a novel adversarial embedding method via stego generation and selection. To improve the diversity of the stego images, this method first randomly generates many candidate stegos according to the amplitudes of the gradients and embedding costs of a given cover. Since the image residuals are the commonly used low-level features in many steganalyzers, the proposed method carefully designs different adaptive high-pass filters to calculate the image residuals, and then selects a final stego from among those candidate stegos which can successfully fool the pre-trained steganalyzer, according to the residual distance between stego and the cover. Extensive experimental evaluations on re-trained CNN-based and traditional steganalyzers demonstrate that the proposed method can significantly enhance the security of the modern steganographic methods in both spatial and JPEG domains, and achieve much better performance than related adversarial embedding methods.