Reversible data hiding scheme in encrypted videos based on vector histogram shifting

doi:10.11772/j.issn.1001-9081.2018071604

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (3): 756-762.DOI: 10.11772/j.issn.1001-9081.2018071604

Previous Articles Next Articles

Reversible data hiding scheme in encrypted videos based on vector histogram shifting

NIU Ke^1,2, ZHANG Shuo³, YANG Xiaoyuan^1,2

1. Key Laboratory of Network and Information Security of the Chinese People's Armed Police Force(Engineering University of the Chinese People's Armed Police Force), Xi'an Shaanxi 710086, China;
2. College of Cryptographic Engineering, Engineering University of the Chinese People's Armed Police Force, Xi'an Shaanxi 710086, China;
3. College of Information and Communication, National University of Defense Technology, Xi'an Shaanxi 710106, China

Received:2018-08-02 Revised:2018-09-19 Online:2019-03-10 Published:2019-03-11
Contact: 杨晓元
Supported by:
This work is partially supported by the Natural Science Foundation of Shaanxi Province (2017JM6113).

基于矢量直方图迁移的视频加密域可逆隐写方案

钮可^1,2, 张硕³, 杨晓元^1,2

1. 网络与信息安全武警部队重点实验室(武警工程大学), 西安 710086;
2. 武警工程大学密码工程学院, 西安 710086;
3. 国防科技大学信息通信学院, 西安 710106

作者简介:钮可(1981-),男,浙江湖州人,副教授,博士研究生,主要研究方向:信息隐藏、多媒体安全;张硕(1988-),女,山东日照人,硕士研究生,主要研究方向:军事通信、信息隐藏;杨晓元(1959-),男,湖南湘潭人,教授,博士生导师,硕士,主要研究方向:信息安全、密码学。
基金资助:
陕西省自然科学基金资助项目（2017JM6113）。

Abstract

Abstract: Aiming at the problem of low embedding capacity and poor invisibility in compressed domain video hiding algorithm, a reversible steganography scheme for H.264/AVC encryption domain was proposed. Firstly, the reference frame interval parameter was determined by the embedded capacity and the carrier size, and whether the cover was encrypted was determined by the need. Then, an embedded key was generated according to the number of video frames to be embedded. Finally, the reversible information embedding on motion vector was realized by the vector histogram shifting in the compressed video. The proposed scheme overcame the distortion accumulation effect due to motion vector modification by specifying a decoding reference frame and is compatible with motion vector-based video encryption algorithms. Video decryption and information extraction depend on the decryption key and the embedded key respectively, which are separated from each other. The information can be extracted in the video ciphertext domain or the decrypted plaintext domain and has no influence on video cover recovery. As security of the information depends on the embedded key, the length of the key can be controlled as needed with the maximum length equal to the number of frames in which the information can be embedded. Experimental results show that the proposed scheme has low computational complexity and high security, and can adjust capacity and invisibility according to embedded load. Compared with BCH code reversible embedding scheme, the PSNR (Peak Signal-to-Noise Ratio) value increases by 3 to 5 dB and the average embedded capacity increases by 5 to 10 times.

Key words: reversible video hiding, encryption domain video, H.264/AVC coding, vector histogram shifting, reference frame modification

摘要： 针对压缩域视频隐藏算法嵌入容量低、不可见性差的问题，提出了一种H.264/AVC加密域的可逆隐写方案。首先由嵌入容量和载体大小决定参考帧间隔参数，并根据需要决定是否对载体进行加密；然后，根据待嵌视频帧数生成嵌入密钥；最后通过压缩视频中矢量直方图迁移，实现运动矢量上的可逆信息嵌入。所提方案通过指定解码参考帧，克服了由于运动矢量修改而造成的失真累加效应。所提方案兼容基于运动矢量的视频加密算法，视频的解密和信息提取分别依赖解密密钥和嵌入密钥，两者之间相互分离，在视频密文域或者解密后的明文域中均能提取信息并无损恢复视频载体。信息的安全性依赖于嵌入密钥，密钥长度可以根据需要控制，最大长度等于可嵌入信息的帧数。实验表明该方案计算复杂度低，安全度高，并可以根据嵌入负载调整容量和不可见性，与BCH码可逆嵌入方案相比PSNR值提高3~5 dB，平均嵌入容量增加5~10倍。

关键词: 可逆视频隐藏, 加密域视频, H.264/AVC编码, 矢量直方图迁移, 参考帧修改

CLC Number:

TP391.41

NIU Ke, ZHANG Shuo, YANG Xiaoyuan. Reversible data hiding scheme in encrypted videos based on vector histogram shifting[J]. Journal of Computer Applications, 2019, 39(3): 756-762.

钮可, 张硕, 杨晓元. 基于矢量直方图迁移的视频加密域可逆隐写方案[J]. 计算机应用, 2019, 39(3): 756-762.

References

[1] AL-QERSHI O M, KHOO B E. Two-dimensional difference expansion (2D-DE) scheme with a characteristics-based threshold[J]. Signal Processing, 2013, 93(1):154-162.
[2] ALATTAR A M. Reversible watermark using the difference expansion of a generalized integer transform[J]. IEEE Transactions on Image Processing, 2004, 13(8):1147-1156.
[3] BARTON J M. Method and apparatus for embedding authentication information within digital data:US, 6523114[P]. 1997-07-08.
[4] HONSINGER C W, JONES P W, RABBANI M, et al. Lossless recovery of an original image containing embedded data:US, 77102/E-D[P]. 2001-08-21.
[5] COATRIEUX G, Le GUILLOU C, CAUVIN J-M, et al. Reversible watermarking for knowledge digest embedding and reliability control in medical images[J]. IEEE Transactions on Information Technology in Biomedicine, 2009, 13(2):158-165.
[6] PENG F, LEI Y, LONG M, et al. A reversible watermarking scheme for two-dimensional CAD engineering graphics based on improved difference expansion[J]. Computer-Aided Design, 2011, 43(8):1018-1024.
[7] HWANG K, LI D. Trusted cloud computing with secure resources and data coloring[J]. IEEE Internet Computing, 2010, 14(5):14-22.
[8] SHI Y-Q, LI X, ZHANG X, et al. Reversible data hiding:advances in the past two decades[J]. IEEE Access, 2016, 4:3210-3237.
[9] TIAN J. Reversible data embedding using a difference expansion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2003, 13(8):890-896.
[10] NI Z, SHI Y Q, ANSARI N, et al. Reversible data hiding[J]. IEEE Transactions on Circuits and System for Video Technology, 2006, 16(3):354-362.
[11] LIU Y, JU L, HU M, et al. A robust reversible data hiding scheme for H.264 without distortion drift[J]. Neurocomputing, 2015,151:1053-1062.
[12] ZHAO J, LI Z-T, FENG B. A novel two-dimensional histogram modification for reversible data embedding in to stereo H.264 video[J]. Multimedia Tools and Applications, 2016, 75(10):5959-5980.
[13] VURAL C, BARAKLI B. Reversible video watermarking using motion-compensated frame interpolation error expansion[J]. Signal, Image and Video Processing, 2015, 9(7):1613-1623.
[14] RICHARDSON I E G. H.264 and MPEG-4 Video Compression:Video Coding for Next-generation Multimedia[M]. Hoboken, NJ:John Wiley & Sons, 2003:171-172.
[15] FALLAHPOUR M, SEDAAGHI M H. High capacity lossless data hiding based on histogram modification[J]. IEICE Electronics Express, 2007, 4(7):205-210.
[16] LEE S-K, SUH Y-H, HO Y-S. Reversiblee image authentication based on watermarking[C]//Proceedings of the 2006 IEEE International Conference on Multimedia and Expo. Washington, DC:IEEE Computer Society, 2006:1321-1324.
[17] WANG J, NI J, ZHANG X, et al. Rate and distortion optimization for reversible data hiding using multiple histogram shifting[J]. IEEE Transactions on Cybernetics, 2017, 47(2):315-326.
[18] 毕厚杰,王健.新一代视频压缩编码标准:H.264/AVC[M].2版.北京:人民邮电出版社,2009:114.(BI H J, WANG J. New Generation Video Compression Coding Standard:H.264/AVC[M]. 2nd ed. Beijing:Posts & Telecom Press, 2009:114.)
[19] WEI S-T, TIEN C-W, LIU B-D, et al. Adaptive truncation algorithm for Hadamard-transformed H.264/AVC lossless video coding[J]. IEEE Transactions on Circuits and System for Video Technology, 2011, 21(5):538-548.
[20] XU D, WANG R, SHI Y Q. Data hiding in encrypted H.264/AVC video streams by codeword substitution[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(4):596-606.
[21] STUTZ T, UHL A. A survey of H.264 AVC/SVC encryption[J]. IEEE Transactions on Circuits and System for Video Technology, 2012, 22(3):325-339.
[22] XU D, WANG R, SHI Y Q. Reversible data hiding in encrypted H.264/AVC video streams[C]//Proceedings of the 2013 International Workshop on Digital Watermarking, LNCS 8389. Berlin:Springer, 2013:141-152.
[23] MUHIT A A, PICKERING M R, FRATER M R, et al. Video coding using elastic motion model and larger blocks[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2010, 20(5):661-672.
[24] MUHIT A A, PICKERING M R, FRATER M R, et al. Video coding using fast geometry-adaptive partitioning and an elastic motion model[J]. Journal of Visual Communication and Image Representation, 2012, 23(1):31-41.

Reversible data hiding scheme in encrypted videos based on vector histogram shifting

基于矢量直方图迁移的视频加密域可逆隐写方案

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	Yue ZHANG, Liang ZHANG, Fei XIE, Jiale YANG, Rui ZHANG, Yijian LIU. Road abandoned object detection algorithm based on optimized instance segmentation model [J]. Journal of Computer Applications, 2021, 41(11): 3228-3233.
[2]	Lin SUN, Yubo YUAN. Drowsiness recognition algorithm based on human eye state [J]. Journal of Computer Applications, 2021, 41(11): 3213-3218.
[3]	Chenyu GE, Liang DONG, Yikun XU, Yi CHANG, Hongming ZHANG. Global-scale radar data restoration algorithm based on total variation and low-rank group sparsity [J]. Journal of Computer Applications, 2021, 41(11): 3353-3361.
[4]	Junhua YAN, Ping HOU, Yin ZHANG, Xiangyang LYU, Yue MA, Gaofei WANG. Multiply distortion type judgement method based on multi-scale and multi-classifier convolutional neural network [J]. Journal of Computer Applications, 2021, 41(11): 3178-3184.
[5]	Fuhai LI, Murong JIANG, Lei YANG, Junyi CHEN. Solar speckle image deblurring method with gradient guidance based on generative adversarial network [J]. Journal of Computer Applications, 2021, 41(11): 3345-3352.
[6]	Jianfang CAO, Minmin YAN, Yiming JIA, Xiaodong TIAN. Application of Inception-v3 model integrated with transfer learning in dynasty identification of ancient murals [J]. Journal of Computer Applications, 2021, 41(11): 3219-3227.
[7]	Guihui CHEN, Huikang LIU, Zhongbing LI, Jiao PENG, Shaotian WANG, Jinyu LIN. Improved algorithm of generative adversarial network based on arbitration mechanism [J]. Journal of Computer Applications, 2021, 41(11): 3185-3191.
[8]	. EE-GAN：Facial expression recognition method based on generative adversarial network and network integration [J]. Journal of Computer Applications, 0, (): 0-0.
[9]	. Point cloud registration algorithm based on residual attention mechanism [J]. Journal of Computer Applications, 0, (): 0-0.
[10]	PEI Yiyao, GUO Huiming, ZHANG Danpu, CHEN Wenbo. Robust 3D object detection method based on localization uncertainty [J]. Journal of Computer Applications, 2021, 41(10): 2979-2984.
[11]	XU Xuebin, ZHANG Jiada, LIU Wei, LU Longbin, ZHAO Yuqing. High-precision classification method for breast cancer fusing spatial features and channel features [J]. Journal of Computer Applications, 2021, 41(10): 3025-3032.
[12]	WEI Chunwu, ZHAO Juanjuan, TANG Xiaoxian, QIANG Yan. Knowledge extraction method for follow-up data based on multi-term distillation network [J]. Journal of Computer Applications, 2021, 41(10): 2871-2878.
[13]	LI Ziqiang, WANG Zhengyong, CHEN Honggang, LI Linyi, HE Xiaohai. Video abnormal behavior detection based on dual prediction model of appearance and motion features [J]. Journal of Computer Applications, 2021, 41(10): 2997-3003.
[14]	WANG Hongyu, ZHANG Yu, YANG Heng, MU Nan. Salient object detection in weak light images based on ant colony optimization algorithm [J]. Journal of Computer Applications, 2021, 41(10): 2970-2978.
[15]	. Joint detection and tracking algorithm of target in aerial refueling [J]. Journal of Computer Applications, 0, (): 0-0.