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SDCN2: A Shallow Densely Connected CNN for Multi-Purpose Image Manipulation Detection

Published:31 October 2022Publication History
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Abstract

Digital image information can be easily tampered with to harm the integrity of someone. Thus, recognizing the truthfulness and processing history of an image is one of the essential concerns in multimedia forensics. Numerous forensic methods have been developed by researchers with the ability to detect targeted editing operations. However, creating a unified forensic approach capable of detecting multiple image manipulations remains a challenging problem. In this article, a new general-purpose forensic approach is designed based on a shallow densely connected convolutional neural network (SDCN2) that exploits local dense connections and global residual learning. The residual domain is considered in the proposed network rather than the spatial domain to analyze the image manipulation artifacts because the residual domain is less dependent on image content information. To attain this purpose, a residual convolutional layer is employed at the beginning of the proposed model to adaptively learn the image manipulation features by suppressing the image content information. Then, the obtained image residuals or prediction error features are further processed by the shallow densely connected convolutional neural network for high-level feature extraction. In addition, the hierarchical features produced by the densely connected blocks and prediction error features are fused globally for better information flow across the network. The extensive experiment results show that the proposed scheme outperforms the existing state-of-the-art general-purpose forensic schemes even under anti-forensic attacks, when tested on large-scale datasets. The proposed model offers overall detection accuracies of 98.34% and 99.22% for BOSSBase and Dresden datasets, respectively, for multiple image manipulation detection. Moreover, the proposed network is highly efficient in terms of computational complexity as compared to the existing approaches.

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    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 3s
      October 2022
      381 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3567476
      • Editor:
      • Abdulmotaleb El Saddik
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 31 October 2022
      • Online AM: 16 March 2022
      • Accepted: 4 January 2022
      • Revised: 4 December 2021
      • Received: 10 March 2021
      Published in tomm Volume 18, Issue 3s

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