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LUO Junwei, YU Mujian, YIN Xiaolin, LU Wei. Binary Image Steganalysis Based on Symmetrical Local Residual Patterns[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2020.00.414
Citation: LUO Junwei, YU Mujian, YIN Xiaolin, LU Wei. Binary Image Steganalysis Based on Symmetrical Local Residual Patterns[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2020.00.414

Binary Image Steganalysis Based on Symmetrical Local Residual Patterns

doi: 10.1049/cje.2020.00.414
Funds:  This work is supported by: National Natural Science Foundation of China (U1736118, U2001202, 62072480) and the Key Areas R&D Program of Guangdong (2019B010136002)
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  • Author Bio:

    received the B.S. degree in information security from Sun Yat-sen University, China, in 2020. He is currently pursuing the M.S. degree with the School of Computer Science and Engineering, Sun Yat-sen University, China. His research interests include multimedia security and data hiding. (Email: luojw8@mail2.sysu.edu.cn)

    received the M.S. degree in computer science and technology from Sun Yat-sen University, China, in 2018. His research interests include multimedia security and data hiding. (Email: yumj8@mail2.sysu.edu.cn)

    received the B.S. degree in software engineering from Sun Yat-sen University, China, in 2018. She is currently pursuing the Ph.D. degree with the School of Computer Science and Engineering, Sun Yat-sen University, China. Her research interests include multimedia security and data hiding. (Email: yinxl6@mail2.sysu.edu.cn)

    (corresponding author) received the B.S. degree in automation from Northeast University, China in 2002, the M.S. degree and the Ph.D. degree in computer science from Shanghai Jiao Tong University, China in 2005 and 2007 respectively. He was a research assistant at Hong Kong Polytechnic University from 2006 to 2007. He is currently a Professor with the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China. His research interests include multimedia forensics and security, data hiding and watermarking, privacy protection. He is an Associate Editor for the Signal Processing and the Journal of Visual Communication and Image Representation. (Email: luwei3@sysu.edu.cn)

  • Received Date: 2020-12-12
  • Accepted Date: 2021-11-08
  • Available Online: 2022-01-07
  • Residual computation is an effective method for gray-scale image steganalysis. For binary images, the residual computation calculated by the XOR operation is also employed in the Local residual patterns (LRP) model for steganalysis. In this paper, a binary image steganalytic scheme based on Symmetrical local residual patterns (SLRP) is proposed. The symmetrical relationships among residual patterns are introduced that make the features more compact while reducing the dimensionality of the features set. Multi-scale windows are utilized to construct three SLRP submodels which are further merged to construct the final features set instead of a single model. What's more, SLRPs with higher probability to be modified after embedding are emphasized and selected to construct the feature sets for training the SVM classifier. Finally, experimental results show that the proposed steganalytic scheme is effective for detecting binary image steganography.
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