Dual-Attention ResUNet-GAN for Secure Image Steganography: Optimizing the Trade-off Between Imperceptibility and Payload Capacity

Secure and high-capacity data concealment has already become a requirement of modern multimedia communication, particularly with the enhanced protection and privacy levels of concern. The framework introduced in this study—the improved Dual-Attention ResUNet-GAN—helps optimize the trade-off among imperceptibility, robustness, and payload capacity in the field of image steganography. The two PatchGAN discriminators used in the model were a visual realism discriminator and a learned steganalyzer. Two encoders based on the ResNet-34 using CBAM-based dual attention are to be used. Just before the data is embedded, AES-256 encryption in CBC mode is employed to provide cryptographic confidentiality. Experiments on the COCO, BOSSbase, and ALASKA2 datasets are conducted to evaluate the proposed method's performance, yielding PSNR=42.5 dB, SSIM=0.98, BER=0.02, and high resistance to steganalysis (PE=91.2% vs. SRNet). Embedding is also changed in the proposed framework to high-entropy areas, thereby allowing the application of both conservative payloads (0.0156 bpp) and capacity-driven configurations (0.4 bpp) without affecting image quality. The findings have validated that the proposed system fits well with secure communication and intelligent data-hiding applications in real-world scenarios.