Efficient Steganography GAN Model with Residual Structures and GCSA Attention

In the field of image steganography, the key issue has always been how to enhance the capacity and security of information hiding while maintaining image quality. This paper introduces an innovative model based on SteganoGAN that employs a Global Channel-Spatial Attention Mechanism (GCSA) and a residual structure in the decoder, significantly enhancing the effectiveness of steganography techniques. By integrating channel and spatial attention, the GCSA mechanism optimizes the recognition and processing of key features, greatly improving the precision and concealment of information hiding. Additionally, the model incorporates a residual structure in the decoder, enhancing learning capabilities and the quality of information recovery, ensuring good image quality even at high embedding rates.