Image steganography with high embedding capacity based on multi-target adversarial attack

Deep learning-based steganography techniques utilizing generative adversarial networks have attracted considerable attention due to their ability to produce realistic images that serve as effective carriers for hidden information. However, as the capacity for embedding information increases, the quality of the generated covert images tends to decline significantly. To address these challenges and enhance both the quality of covert images and data-hiding performance, we propose a high-capacity image steganography method known as Multi-Target Adversarial Image Steganography (MTAIS). This method leverages a multi-target adversarial attack technique to effectively conceal high-capacity secret information within images. The proposed scheme adapts the fully connected layer of the recognition model and transforms the undirected adversarial attack into a directed adversarial attack targeting multiple outputs, allowing for fine-tuning of the base model without extensive retraining. We conducted comprehensive experiments to benchmark the proposed scheme against several established deep learning-based steganography schemes. The results indicate that the proposed scheme consistently outperforms its competitors across various evaluation metrics. Notably, our method preserves the quality of the cover image, ensuring visual integrity while achieving a high capacity for embedding secret information. The experimental results underscore the advantages of the proposed scheme in terms of performance and efficiency, establishing it as a robust solution for high-capacity image steganography.