首页|Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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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 dif-ferent 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.

Color image forensicsJPEGJPEG2000Mixed double compressionQuaternion convolutional neural network

Hao WANG、Jinwei WANG、Xuelong HU、Bingtao HU、Qilin YIN、Xiangyang LUO、Bin MA、Jinsheng SUN

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Department of Automation,Nanjing University of Science and Technology,Nanjing 210044,China

Engineering Research Center of Digital Forensics,Ministry of Education,Nanjing 210044,China

Department of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China

School of Management,Nanjing University of Posts and Telecommunications,Nanjing 210044,China

Department of Computer Science and Engineering,Guangdong Province Key Laboratory of Information Security Technology,Sun Yat-sen University,Guangzhou 510006,China

State Key Laboratory of Mathematical Engineering and Advanced Computing,Zhengzhou 450001,China

Qilu University of Technology,Jinan 250353,China

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

620722506177228161702235U1636117U1804263

2024

10.23919/cje.2022.00.179

电子学报(英文)

电子学报(英文)

CSTPCDEI
ISSN：1022-4653
年,卷(期)：2024.33(3)