Enhancing steganography capacity through multi-stage generator model in generative adversarial network based image concealment

Abstract

Traditional steganography algorithms use procedures created by human experts to conceal the secret message inside a cover medium. Generative adversarial networks (GANs) have recently been used to automate this process. However, GAN based steganography has some limitations. The capacity of these models is limited. By increasing the steganography capacity, security is decreased, and distortion is increased. The performance of the extractor network also decreases with increasing the steganography capacity. In this work, an approach for developing a generator model for image steganography is proposed. The approach involves building a generator model, called the late embedding generator model, in two stages. The first stage of the generator model uses only the flattened cover image, and second stage uses a secret message and the first stage's output to generate the stego image. Furthermore, a dual-training strategy is employed to train the generator network: the first stage focuses on learning fundamental image features through a reconstruction loss, and the second stage is trained with three loss terms, including an adversarial loss, to incorporate the secret message. The proposed approach demonstrates that hiding data only in the deeper layers of the generator network boosts capacity without requiring complex architectures, reducing computational storage requirements. The efficacy of the proposed approach is evaluated by varying the depth of these two stages, resulting in four generator models. A comprehensive set of experiments was performed on the CelebA dataset, which contains more than 200,000 samples. The results show that the late embedding model performs better than the state-of-the-art models. Also, it increases the steganography capacity to more than four times compared with the existing GAN-based steganography methods. The extracted payload achieves an accuracy of 99.98%, with the extractor model successfully decoding the secret message.