Enhancing JPEG Steganography With GANs via Adaptive Modification Loss and Random Masking

Learning adaptive embedding costs with deep learning has become an important direction for improving steganographic security. However, most existing approaches focus on spatial-domain images, while effective cost learning for JPEG images remains challenging due to the complexity introduced by block-wise discrete cosine transform and lossy quantization. In this paper, we present an enhanced GAN-based framework that learns asymmetric embedding costs for JPEG images from scratch, improving both content adaptivity and training stability. Specifically, we introduce an adaptive modification loss that directly links the generator’s predicted embedding probabilities to the actual modification behavior, enabling fully data-driven and content-aware optimization without relying on handcrafted filters or quantization-dependent heuristics. In addition, we propose a random masking strategy applied during later training stages to prevent discriminator dominance and maintain informative adversarial feedback. Extensive experiments on multiple benchmark datasets and under various steganalytic detectors demonstrate that the proposed method consistently improves steganographic security over existing JPEG-domain approaches. Ablation studies further validate the effectiveness of the proposed loss design and training strategy.