research-article

Noise and Edge Based Dual Branch Image Manipulation Detection

Authors:

Zhongyuan Zhang,

Juan ZhaoAuthors Info & Claims

CNIOT '23: Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things

Pages 963 - 968

https://doi.org/10.1145/3603781.3604221

Published: 27 July 2023 Publication History

Abstract

Unlike ordinary computer vision tasks that focus more on the semantic content of images, the image manipulation detection task pays more attention to the subtle information of image manipulation. In this paper, the noise image extracted by the improved constrained convolution is used as the input of the model instead of the original image to obtain more subtle traces of manipulation. Meanwhile, the dual branch network, consisting of a high-resolution branch and a context branch, is used to capture the traces of artifacts. In general, most manipulation leaves artifacts on the manipulation region boundary. A specially designed manipulation edge detection module is constructed based on the dual branch network to identify these artifacts. We add a distance factor to the self-attention module to better describe the correlation between pixels. Experimental results on publicly available image manipulation datasets demonstrate the effectiveness of our model.

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Cited By

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https://doi.org/10.48175/IJARSCT-18605
Li FZhai HLiu TZhang XQin C(2024)Learning Compressed Artifact for JPEG Manipulation Localization Using Wide-Receptive-Field NetworkACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367888320:10(1-23)Online publication date: 18-Jul-2024
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Li RZhu JLuo XMa Z(2024)Image Tampering Detection Method Based on Multi-Feature Fusion2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C63300.2024.00051(345-356)Online publication date: 1-Jul-2024
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Index Terms

Noise and Edge Based Dual Branch Image Manipulation Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Computer graphics

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

CNIOT '23: Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things

May 2023

1025 pages

ISBN:9798400700705

DOI:10.1145/3603781

Copyright © 2023 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2023

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MoE-CMCC Artificial Intelligence Project

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CNIOT'23

CNIOT'23: 2023 4th International Conference on Computing, Networks and Internet of Things

May 26 - 28, 2023

Xiamen, China

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Overall Acceptance Rate 39 of 82 submissions, 48%

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Cited By

P. Maheshwaran P. Kavitha S. Kamalakkannan (2024)Camera Vision Based Trash Classification and Detection System using Deep LearningInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-18605(21-26)Online publication date: 30-May-2024
https://doi.org/10.48175/IJARSCT-18605
Li FZhai HLiu TZhang XQin C(2024)Learning Compressed Artifact for JPEG Manipulation Localization Using Wide-Receptive-Field NetworkACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367888320:10(1-23)Online publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1145/3678883
Li RZhu JLuo XMa Z(2024)Image Tampering Detection Method Based on Multi-Feature Fusion2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C63300.2024.00051(345-356)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS-C63300.2024.00051
Dagar DVishwakarma D(2024)Shallowfake and deepfake image manipulation localization using noise and RGB-based dual branch methodSignal, Image and Video Processing10.1007/s11760-024-03376-x18:10(7065-7077)Online publication date: 25-Jun-2024
https://doi.org/10.1007/s11760-024-03376-x
Zhou XWei WYu XPei R(2024)Multi-scale Image Tampering Detection Using Inception-UNet NetworkData Science10.1007/978-981-97-8749-4_23(306-315)Online publication date: 31-Oct-2024
https://doi.org/10.1007/978-981-97-8749-4_23
Huang JYuan XKe WLam C(2024)Dual Hypergraph Convolution Networks for Image Forgery LocalizationPattern Recognition10.1007/978-3-031-78312-8_22(334-345)Online publication date: 4-Dec-2024
https://doi.org/10.1007/978-3-031-78312-8_22
Shi CChen LWang CZhou XQin Z(2023)Review of Image Forensic Techniques Based on Deep LearningMathematics10.3390/math1114313411:14(3134)Online publication date: 16-Jul-2023
https://doi.org/10.3390/math11143134
Das SAmin M(2023)Learning Interpretable Forensic Representations via Local Window Modulation2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)10.1109/ICCVW60793.2023.00050(436-447)Online publication date: 2-Oct-2023
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