A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

A HEVC Video Steganalysis Algorithm Based on PU Partition Modes

Steganalysis is a technique used for detecting the existence of secret information embedded into cover media such as images and videos.Currently,with the higher speed of the Internet,videos have become a kind of main methods for transferring information.The latest video coding standard High Efficiency Video Coding(HEVC)shows better coding performance compared with the H.264/AVC standard published in the previous time.Therefore,since the HEVC was published,HEVC videos have been widely used as carriers of hidden information.In this paper,a steganalysis algorithm is proposed to detect the latest HEVC video steganography method which is based on the modification of Prediction Units(PU)partition modes.To detect the embedded data,All the PU partition modes are extracted from P pictures,and the probability of each PU partition mode in cover videos and stego videos is adopted as the classification feature.Furthermore,feature optimization is applied,that the 25-dimensional steganalysis feature has been reduced to the 3-dimensional feature.Then the Support Vector Machine(SVM)is used to identify stego videos.It is demonstrated in experimental results that the proposed steganalysis algorithm can effectively detect the stego videos,and much higher classification accuracy has been achieved compared with state-of-the-art work.

Steganalysis of MSU Stego Video Based on Block Matching of Interframe Collusion and Motion Detection

MSU Stego Video is a public video steganographic tool, which has strong robustness and is regarded as a real video steganographic tool. In order to increase the detection rate, this paper proposes a new steganoalysis method against MSU, which uses the chessboard character of MSU embedded video, proposes a down-sample block-based collusion method to estimate the original frame and checks the chessboard mode of the different frame between tested frame and estimated frame to detect MSU steganographic evidences. To reduce the error introduced by severe movement of the video content, a method that abandons severe motion blocks from detecting is proposed. The experiment results show that the false negative rate of the proposed algorithm is lower than 5%, and the false positive rate is lower than 2%. Our algorithm has significantly better performance than existing algorithms. Especially to the video that has fast motion, the algorithm has more remarkable performance.