Successful adaptive steganography has mainly focused on embedding the payload while minimizing an appropriately defined distortion function. The application of deep learning to steganalysis has greatly challenged present adaptive steganographic methods, but has also shown the potential for the improvement of steganography. This paper proposes a distortion function generating a framework for steganography. It has three modules: a generator with a U-Net architecture to translate a cover image into an embedding change probability map, a no-pre-training-required double-tanh function to approximate the optimal embedding simulator while preserving gradient norm during backpropagation in the adversarial training, and an enhanced steganalyzer based on a convolution neural network together with multiple high pass filters as the discriminator. Extensive experimental results on different datasets have shown that the proposed framework outperforms the current state-of-the-art steganographic schemes. Moreover, the adversarial training time is reduced dramatically compared with the GAN-based automatic steganographic distortion learning framework (ASDL-GAN).

Current mainstream methods for digital image steganography are content adaptive. That is, the secret messages are embedded in the complicated region in the cover image while minimizing the embedding distortion so as to suppress statistical detectability. Since there is already a practical encoding scheme for data embedding near the payload-distortion bound, the design of the embedding cost function becomes a deterministic part in steganography. Unlike the traditional heuristic hand-crafted method, this paper proposes a novel generative adversarial network based framework to automatically learn the embedding cost function for JPEG steganography. The proposed framework consists of a generator, a gradient-descent friendly inverse discrete cosine transformation module, an embedding simulator and a discriminator for steganalysis. Through training the generator and discriminator in alternation, the embedding cost function can finally be obtained by the trained generator. Experimental results demonstrate that our method can automatically learn a reasonable embedding cost function and achieve a satisfying performance.