Color Image Steganography Using Generative Adversarial Networks with a Phased Training Strategy

Most existing steganographic techniques are primarily designed for grayscale images.When directly applied to color images without considering inter-channel color interactions, their security can be significantly compromised.In this paper, we propose a novel color image steganography method based on generative adversarial networks (GANs).Our framework features a dual-branch generator and a discriminator equipped with multiple steganalytic networks, each focused on a specific color channel.This architecture enables the progressive learning of asymmetric embedding costs across channels from scratch.We also introduce a phased training strategy that facilitates the learning of inter-channel color interactions and optimizes the security of each color channel in distinct phases, improving overall security.Furthermore, we propose a new adaptive update strategy and introduce the Mean Absolute Deviation (MAD) loss function to maintain a dynamic balance between the generator and discriminator, thereby progressively enhancing the generator's steganographic performance during training.Extensive comparative experiments demonstrate that the proposed method achieves state-of-the-art security against color image steganalysis.Comprehensive ablation studies further validate the effectiveness and rationale behind our approach.