SCGM: Asymmetric Steganographic Embedding Cost Learning With Adaptive Modulation

Xingjie Dai, Ziwen He, Xiang Zhang, Zhangjie Fu

Abstract: Recently, the asymmetric cost-based steganographic method using generative adversarial networks has achieved significant success. This highlights the substantial potential of deep learning-based asymmetric cost generation methods over traditional methods reliant on cost enhancement. However, the current frameworks for asymmetric cost learning ignore the correlation between positive and negative embedding costs, resulting in an imbalance asymmetric embedding costs. This can cause scattered modified pixels or even anomalous modified pixels in the stego image, thereby reducing steganographic security. In this paper, we propose a novel asymmetric steganographic cost learning framework, termed Steganographic embedding Cost Generation and Modulation (SCGM), to ensure a balance between asymmetric embedding costs by maintaining the correlation and therefore improve steganographic security. In our framework, we initially train a policy network to produce symmetric costs and subsequently use an adaptive modulation module we designed to achieve asymmetry. The modulation module facilitates the adaptive transformation of learned symmetric costs into asymmetric costs by autonomously learning modulation proportions during adversarial training with steganalysis. Moreover, we develop distinct adversarial loss functions for both the symmetric cost generation and the asymmetric cost modulation phases to further enhance steganographic security. Extensive experimental results have demonstrated that SCGM attains state-of-the-art performance in steganographic security, with an average error rate across steganalyzers that exceeds the existing best asymmetric cost-based steganography method by 2.77%.

External IDs:doi:10.1109/tcsvt.2024.3427142