Jun Li

Jun Li, Minqing Zhang, Ke Niu et al.

Among steganalysis techniques, detection against motion vector (MV) domain-based video steganography in High Efficiency Video Coding (HEVC) standard remains a hot and challenging issue. For the purpose of improving the detection performance, this paper proposes a steganalysis feature based on the optimality of predicted MVs with a dimension of one. Firstly, we point out that the motion vector prediction (MVP) of the prediction unit (PU) encoded using the Advanced Motion Vector Prediction (AMVP) technique satisfies the local optimality in the cover video. Secondly, we analyze that in HEVC video, message embedding either using MVP index or motion vector differences (MVD) may destroy the above optimality of MVP. And then, we define the optimal rate of MVP in HEVC video as a steganalysis feature. Finally, we conduct steganalysis detection experiments on two general datasets for three popular steganography methods and compare the performance with four state-of-the-art steganalysis methods. The experimental results show that the proposed optimal rate of MVP for all cover videos is 100\%, while the optimal rate of MVP for all stego videos is less than 100\%. Therefore, the proposed steganography scheme can accurately distinguish between cover videos and stego videos, and it is efficiently applied to practical scenarios with no model training and low computational complexity.

Jia Liu, Yan Ke, Yu Lei et al.

In order to keep the data secret, various techniques have been implemented to encrypt and decrypt the secret data. Cryptography is committed to the security of content, i.e. it cannot be restored with a given ciphertext. Steganography is to hiding the existence of a communication channel within a stego. However, it has been difficult to construct a cipher (cypher) that simultaneously satisfy both channel and content security for secure communication. Inspired by the Cardan grille, this paper presents a new generative framework for grille cipher. A digital cardan grille is used for message encryption and decryption. The ciphertext is directly sampled by a powerful generator without an explicit cover. Message loss and prior loss are proposed for penalizing message extraction error and unrealistic ciphertext. Jensen-Shannon Divergence is introduced as new criteria for channel security. A simple practical data-driven grille cipher is proposed using semantic image inpainting and generative adversarial network. Experimental results demonstrate the promising of the proposed method.