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

Computers, Materials & Continua 2024年第5期79卷 2085-2103页

作者： Jun Li Minqing Zhang Ke Niu Yingnan Zhang Xiaoyuan Yang College of Cryptography Engineering Engineering University of the Chinese People’s Armed Police ForceXi’an710086China Key Laboratory of Network and Information Security of the Chinese People’s Armed Police Force Xi’an710086China

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.

关键词： Video steganography video steganalysis motion vector prediction motion vector difference advanced motion vector prediction local optimality

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Trajectory Tracking Control Based on RTO Enforcing SCFO for Lower Limb Rehabilitation Robot

Trajectory Tracking Control Based on RTO Enforcing SCFO for ...

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第40届中国控制会议

作者： Qinghao Chu Jun Zhang Zhaoxin Chen College of Artificial Intelligence Shenyang University of Technology

In this paper, the link angle trajectory tracking of a Lower Limb Rehabilitation Robot（LLRR） under the passive training stage has been investigated. To deal with the mismatch between the model and the actual controlled object, a trajectory tracking control method based on RTO（Real Time Optimization） enforcing SCFO（Sufficient Condition for Feasibility and optimality） for a LLRR is proposed, in which the use of SCFO is used to ensure that the trajectory tracking control results have desirable feasibility. The proposed method has been verified by the simulation experiments of the LLRR model. The simulation results show that the proposed method can accurately track the reference trajectory and moreover has good robustness and anti-interference ability. Compared with the traditional control method, the proposed method is effective to deal with the inevitable model uncertainty for a LLRR and has the characteristics of simple control structure and fast convergence speed.

关键词： lower limb rehabilitation robot RTO SCFO local optimality robustness anti-interference ability

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