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Hao WANG, Jinwei WANG, Xuelong HU, et al., “Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–15, 2024 doi: 10.23919/cje.2022.00.179
Citation: Hao WANG, Jinwei WANG, Xuelong HU, et al., “Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–15, 2024 doi: 10.23919/cje.2022.00.179

Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

doi: 10.23919/cje.2022.00.179
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  • Author Bio:

    Hao WANG received his B.S. in IoT Engineering from Binjiang College, Nanjing University of Information Science & Technology in 2017. He received his M.S. in Computer Science and Technology from Computer Science and Software College, Nanjing University of Information Science & Technology in 2020. He is pursuing Ph.D. degree in Information Security from Nanjing University of Science & Technology. His research interests include multimedia forensics and JPEG compression. (Email: sa875923372@163.com)

    Jinwei WANG is a professor from Nanjing University of Information Science & Technology. He received B.S. degree in Automatic Control from Inner Mongo-lia University of Technology in 2000. He received Ph.D. in Information Security at Nanjing University of Science & Technology in 2007 and was a visiting scholar in Service Anticipation Multimedia Innova-tion (SAMI) Lab of France Telecom R&D Center (Beijing) in 2006. He worked as a senior engineer at the 28th research institute, CETC from 2007 to 2010. He worked as a visiting scholar at New Jersey Institute of Technology, NJ, USA from 2014 to 2015. His research interests include multimedia copyright protection, multimedia forensics, multimedia encryption and data authentication. He has published more than 50 papers, hosted and participated in more than 10 projects. (Email: wjwei_2004@163.com)

    Xuelong HU is a an Associate professor in Nanjing University of Posts and Telecommunications. He has been a PI of the National Natural Science Foundation of China and Natural Science Foundation of JiangSu Provience. His main research fields are systems engineering and quality management. He has published more than 50 papers in some SCI indexed journals. (Email: hxl0419@njupt.edu.cn)

    Bingtao HU received the B.S. degree in Network Engineering from the Nanjing University of Information Science and Technology, in 2018. He received his M.S. in Computer Science and Technology from Computer Science and Software College, Nanjing University of Information Science & Technology in 2022. His research interests include deep learning and JPEG compressio. (Email: 318043256@qq.com)

    Qilin YIN received his B.S. degree in Network Engineering from the Nanjing University of Information Science and Technology, in 2018. He received his M.S. in Computer Science and Technology from Computer Science and Software College, Nanjing University of Information Science & Technology in 2021. He is pursuing Ph.D. degree in Information Security from Sun Yat-sen University. His research interests include multimedia forensics and deepfake. (Email: qilinyin1995@163.com)

    Xiangyang LUO received his B.S., M.S., and Ph.D degrees from the State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China, in 2001, 2004, and 2010, respectively. He is the author or co-author of more than 100 refereed international journal and conference papers. He is currently an associate professor of the State Key Laboratory of Mathematical Engineering and Advanced Computing. His research interests are image steganography and steganalysis technique. (Email: luoxy_ieu@sina.com)

    Bin MA received the M.S. and Ph.D. degrees from Shandong University, Jinan, China, in 2005 and 2008, respectively. From 2008 to 2013, he was an Associate Professor with the School of Information Science, Shandong University of Political Science and Law, Jinan. He visited the New Jersey Institute of Technology at Newark, NJ, USA, as a Visiting Scholar, from 2013 to 2015. He is currently a Professor with the School of Cyber Security, Qilu University of Technology (Shandong Academy of Sciences), Shandong, China. He is also with the Shandong Provincial Key Laboratory of Computer Networks, Jinan. His research interests include reversible data hiding, multimedia security, and image processing. He serves as an Editorial Board Member of a few journals, such as the IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, the Journal of Visual Communication and Image Representation, and the IEEE SIGNAL PROCESSING. He is a member of ACM. (Email: sddxmb@126.com)

    Jinsheng SUN has been with the School of Automation, NJUST, where he is currently a professor, since 1995. He received his B.S., M.S. and Ph.D. of Control Science & Engineering from NJUST in 1990, 1992 and 1995. From 2007 to 2009, he visited the University of Melbourne as a Research Fellow at the Department of Electrical and Electronic Engineering. And in 2011, he was with the City University of Hong Kong as a Senior Research Fellow. His research activity includes network congestion control, information security, quality control and distributed control of multi-agent systems. (Email: jssun67@163.com)

  • Corresponding author: Email: sa875923372@163.com
  • Received Date: 2022-05-27
  • Accepted Date: 2023-05-23
  • Available Online: 2023-07-05
  • Detection of color images that have undergone double compression is a critical aspect of digital image forensics. Despite the existence of various methods capable of detecting double joint photographic experts group (JPEG) compression, they are unable to address the issue of mixed double compression resulting from the use of different compression standards. In particular, the implementation of joint photographic experts group 2000 (JPEG2000) as the secondary compression standard can result in a decline or complete loss of performance in existing methods. To tackle this challenge of JPEG+ JPEG2000 compression, a detection method based on quaternion convolutional neural networks (QCNN) is proposed. The QCNN processes the data as a quaternion, transforming the components of a traditional convolutional neural network (CNN) into a quaternion representation. The relationships between the color channels of the image are preserved, and the utilization of color information is optimized. Additionally, the method includes a feature conversion module that converts the extracted features into quaternion statistical features, thereby amplifying the evidence of double compression. Experimental results indicate that the proposed QCNN-based method improves, on average, by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.
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