MAT-GAN: Adversarial Image Steganography with Secret-Conditioned Generation

Abstract

Image steganography requires models that preserve secret-conditioned structure while producing outputs aligned with the statistics of plausible carrier imagery. We present MAT-GAN, a transformer-guided adversarial framework for secret-conditioned carrier-domain image generation. MAT-GAN combines a convolutional encoder-decoder, a tokenized self-attention bottleneck, and a configurable discriminator with an objective that balances adversarial alignment and reconstruction regularization. We evaluate the method on a RESISC45-derived benchmark that groups urban scenes as carrier-like targets and natural scenes as secret-like inputs, and compare it against DualGAN, CycleGAN, and WGAN under a common reduced training schedule. Across models, DualGAN achieves the strongest common carrier-domain FID of 344.29, while MAT-GAN reaches 473.33. However, ablation within the MAT-GAN family shows that the full model consistently outperforms all partial variants, improving FID to 463.76, reducing proxy MAE to 0.0870, and raising proxy PSNR to 19.27 dB. Detailed quantitative, qualitative, and training-curve analyses indicate that reconstruction regularization is the clearest isolated gain and that transformer guidance becomes most effective when paired with the full objective. These results position MAT-GAN as a compact steganography-oriented adversarial framework for studying secret-conditioned carrier-domain generation.