Steganography aims to hide secret data into a cover media by subtle perturbation. Recently, deep neural network (DNN) based steganography has attracted a lot of research interests. Most of the existing DNN-based steganographic schemes design the secret encoder and decoder in an asymmetrical manner, the performance of which is heavily dependent on the content of cover media. In addition, they are weak in preventing unauthorized data extraction from the stego-media (i.e., the media with hidden data). To address these two issues, we propose a novel DNN steganographic framework termed the Cover Independent DNN Steganography (CIDS). In our CIDS, we take advantage of the powerful generative models to obtain AI-generated cover media for both the sender and receiver according to a key. Then, we propose a pair of symmetrical secret encoder and decoder to conduct a bijective transformation between the secrets and perturbations. While the perturbation is combined with the cover media to produce the stego-media. Such a secret encoding/decoding strategy focuses only on the secrets and is independent of the cover media. We also propose to encrypt the perturbations to effectively prevent unauthorized data extraction. Comprehensive experiments demonstrate the advantage of our CIDS over the state-of-the-art approaches for image steganography.

Steganography is the art of hiding secret data into the cover media for covert communication. In recent years, more and more deep neural network (DNN)-based steganographic schemes are proposed to train steganographic networks for secret embedding and recovery, which are shown to be promising. Compared with the handcrafted steganographic tools, steganographic networks tend to be large in size. It raises concerns on how to imperceptibly and effectively transmit these networks to the sender and receiver to facilitate the covert communication. To address this issue, we propose in this paper a Purified and Unified Steganographic Network (PUSNet). It performs an ordinary machine learning task in a purified network, which could be triggered into steganographic networks for secret embedding or recovery using different keys. We formulate the construction of the PUSNet into a sparse weight filling problem to flexibly switch between the purified and steganographic networks. We further instantiate our PUSNet as an image denoising network with two steganographic networks concealed for secret image embedding and recovery. Comprehensive experiments demonstrate that our PUSNet achieves good performance on secret image embedding, secret image recovery, and image denoising in a single architecture. It is also shown to be capable of imperceptibly carrying the steganographic networks in a purified network. Code is available at https://github.com/albblgb/PUSNet

Image steganography is the art of concealing secret information in images in a way that is imperceptible to unauthorized parties. Recent advances show that is possible to use a fixed neural network (FNN) for secret embedding and extraction. Such fixed neural network steganography (FNNS) achieves high steganographic performance without training the networks, which could be more useful in real-world applications. However, the existing FNNS schemes are vulnerable in the sense that anyone can extract the secret from the stego-image. To deal with this issue, we propose a key-based FNNS scheme to improve the security of the FNNS, where we generate key-controlled perturbations from the FNN for data embedding. As such, only the receiver who possesses the key is able to correctly extract the secret from the stego-image using the FNN. In order to improve the visual quality and undetectability of the stego-image, we further propose an adaptive perturbation optimization strategy by taking the perturbation cost into account. Experimental results show that our proposed scheme is capable of preventing unauthorized secret extraction from the stego-images. Furthermore, our scheme is able to generate stego-images with higher visual quality than the state-of-the-art FNNS scheme, especially when the FNN is a neural network for ordinary learning tasks.