Color Image Information Hiding Based on Attention GAN

Image steganography aims to embed secret information into images to achieve data steganography. Aiming at the problems of insufficient feature expression ability, poor model flexibility, and poor quality of steganographic images in existing methods, this paper proposes an image steganography method based on attention generative adversarial networks. By introducing a combination of global attention mechanism, adaptive convolution module, and deep convolution block, the feature expression ability and the accuracy of image restoration are improved, while a customized discriminator is used to ensure the quality and authenticity of the steganographic image. The adaptive convolution module is able to dynamically adjust the size and number of convolution kernels according to the features of the input image, which effectively improves the flexibility and generalization ability of the model and reduces the consumption of computational resources. In this paper, experimental validation is carried out on COCO, CelebA and ImageNet datasets, and the results show that the method has obvious advantages over existing deep learning steganography techniques in terms of steganographic capacity, security, robustness and imperceptibility.