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ME: Multi-Task Edge-Enhanced for Image Forgery Localization

Published: 03 May 2024 Publication History

Abstract

The key challenge of image splicing detection is how to identify forged regions by capturing subtle traces from high-quality discriminative. In this paper, we propose two feature extraction branches for image forgery localization, and it is named a Multi-Task Edge-Enhanced Network (ME-Net). Firstly, two feature extraction branches are elaborately designed, taking advantage of the discriminative stacked ConvNeXt and ResNet layers, for both RGB and noise domain features. The two feature extraction branches have different architectures and extract edge features in the RGB domain. Secondly, a Fusion Module of Pyramid Split Double Attention (PSDA) is proposed to effectively fuse hierarchical features from two different domains. Thirdly, an Edge Enhancement Position Attention (EEPA) fusion module is proposed to integrate edge context information into the decoding process, thereby enhancing edge details. Finally, an edge feature fusion Pyramid decoder is constructed for feature reconstruction to generate the predicted mask. Extensive experimental results on CASIA v1.0, CASIA v2.0, Columbia, and NIST2016 demonstrate that the proposed method can accurately localize tampered regions in pixel levels and outperform state-of-the-art methods.

References

[1]
Hany Farid. 2009. Image forgery detection. in IEEE Signal Processing Magazine, vol. 26, no. 2, pp. 16-25, March 2009, https://doi.org/10.1109/MSP.2008.931079.
[2]
Tiago José de Carvalho, Christian Riess, Elli Angelopoulou, Hélio Pedrini, and Anderson de Rezende Rocha. 2013. Exposing Digital Image Forgeries by Illumination Color Classification. in IEEE Transactions on Information Forensics and Security, vol. 8, no. 7, pp. 1182-1194, July 2013, https://doi.org/10.1109/TIFS.2013.2265677.
[3]
Xin Liao, Kaide Li, Xinshan Zhu, and K. J. Ray Liu. 2020. Robust Detection of Image Operator Chain With Two-Stream Convolutional Neural Network. in IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 5, pp. 955-968, Aug. 2020, https://doi.org/10.1109/JSTSP.2020.3002391.
[4]
Muhammad Salihin Saealal, Mohd Zamri Ibrahim, Marlina Yakno, and Nurul Wahidah Arshad, "Three-Dimensional Convolutional Approaches for the Verification of Deepfake Videos: The Effect of Image Depth Size on Authentication Performance," Journal of Advances in Information Technology, Vol. 14, No. 3, pp. 488-494, 2023.
[5]
Giovanni Chierchia, Giovanni Poggi, Carlo Sansone, and Luisa Verdoliva. 2013. PRNU-based forgery detection with regularity constraints and global optimization. 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP), Pula, Italy, 2013, pp. 236-241, https://doi.org/10.1109/MMSP.2013.6659294.
[6]
Pasquale Ferrara, Tiziano Bianchi, Alessia De Rosa, and Alessandro Piva. 2012. Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts. in IEEE Transactions on Information Forensics and Security, vol. 7, no. 5, pp. 1566-1577, Oct. 2012, https://doi.org/10.1109/TIFS.2012.2202227.
[7]
Xiuli Bi, Yang Wei, Bin Xiao, and Weisheng Li. 2019. RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 2019, pp. 30-39, https://doi.org/10.1109/CVPRW.2019.00010.
[8]
Yue Wu, Wael AbdAlmageed, and Premkumar Natarajan. 2019. ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries with Anomalous Features. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 9535-9544, https://doi.org/10.1109/CVPR.2019.00977.
[9]
Yu Sun, Rongrong Ni, and Yao Zhao. 2022. ET: Edge-Enhanced Transformer for Image Splicing Detection. in IEEE Signal Processing Letters, vol. 29, pp. 1232-1236, 2022, https://doi.org/10.1109/LSP.2022.3172617.
[10]
Giovanni Chierchia, Giovanni Poggi Carlo Sansone, and Luisa Verdoliva. 2014. A Bayesian-MRF Approach for PRNU-Based Image Forgery Detection. in IEEE Transactions on Information Forensics and Security, vol. 9, no. 4, pp. 554-567, April 2014, https://doi.org/10.1109/TIFS.2014.2302078.
[11]
Peng Zhou, Xintong Han, Vlad I. Morariu, Larry S. Davis. 2018. Learning Rich Features for Image Manipulation Detection. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 1053-1061, https://doi.org/10.1109/CVPR.2018.00116.
[12]
Belhassen Bayar, and Matthew C. Stamm. 2018. Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection. in IEEE Transactions on Information Forensics and Security, vol. 13, no. 11, pp. 2691-2706, Nov. 2018, https://doi.org/10.1109/TIFS.2018.2825953.
[13]
Xuefeng Hu, Zhihan Zhang, Zhenye Jiang, Syomantak Chaudhuri, Zhenheng Yang, and Ram Nevatia. 2020. SPAN: Spatial pyramid attention network for image manipulation localization. Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI 16. Springer International Publishing, 2020.
[14]
Xinru Chen, Chengbo Dong, Jiaqi Ji, Juan Cao, and Xirong Li. 2021. Image Manipulation Detection by Multi-View Multi-Scale Supervision. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 14165-14173, https://doi.org/10.1109/ICCV48922.2021.01392.
[15]
Xiaohong Liu, Yaojie Liu, Jun Chen, and Xiaoming Liu. 2022. PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization. in IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 11, pp. 7505-7517, Nov. 2022, https://doi.org/10.1109/TCSVT.2022.3189545.
[16]
Myung-Joon Kwon, In-Jae Yu, Seung-Hun Nam, and Heung-Kyu Lee. 2021. CAT-Net: Compression Artifact Tracing Network for Detection and Localization of Image Splicing. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2021, pp. 375-384, https://doi.org/10.1109/WACV48630.2021.00042.
[17]
O. R. Vincent and O. Folorunso. 2009. A descriptive algorithm for sobel image edge detection. Proceedings of informing science & IT education conference (InSITE). 2009, 40: 97-107.
[18]
Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. 2019. Dual Attention Network for Scene Segmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 3141-3149, https://doi.org/10.1109/CVPR.2019.00326.
[19]
Hu Zhang, Keke Zu, Jian Lu, Yuru Zou and Deyu Meng. 2022. EPSANet: An efficient pyramid squeeze attention block on convolutional neural network. Proceedings of the Asian Conference on Computer Vision. 2022: 1161-1177.
[20]
Pieter-Tjerk De Boer, Dirk P. Kroese, Shie Mannor, and Reuven Y. Rubinstein. 2005. A tutorial on the cross-entropy method. J. A tutorial on the cross-entropy method. Annals of operations research, 134, 19-67.
[21]
Zhou Wang, Eero P. Simoncelli and Alan C. Bovik, 2003. Multiscale structural similarity for image quality assessment. The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, Pacific Grove, CA, USA, pp. 1398-1402 Vol.2.
[22]
Gellért Máttyus, Wenjie Luo, and Raquel Urtasun. 2017. DeepRoadMapper: Extracting Road Topology from Aerial Images. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp. 3458-3466, https://doi.org/10.1109/ICCV.2017.372.
[23]
Jing Dong, Wei Wang, and Tieniu Tan. 2013. CASIA Image Tampering Detection Evaluation Database. 2013 IEEE China Summit and International Conference on Signal and Information Processing, Beijing, China, 2013, pp. 422-426.
[24]
Jianbo Shi and Jitendra Malik. 2000. Normalized cuts and image segmentation. in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000, https://doi.org/10.1109/34.868688.
[25]
Zenan Shi, Xuanjing Shen, Haipeng Chen, and Yingda Lyu. 2020. Global Semantic Consistency Network for Image Manipulation Detection. in IEEE Signal Processing Letters, vol. 27, pp. 1755-1759, 2020, https://doi.org/10.1109/LSP.2020.3026954.
[26]
Yang Wei, Jianfeng Ma, Zhuzhu Wang, Bin Xiao, and Wenying Zheng. 2022. Image splicing forgery detection by combining synthetic adversarial networks and hybrid dense U‐net based on multiple spaces. J. International Journal of Intelligent Systems, June 2022, 37(11): 8291-8308. https://doi.org/10.1002/int.22939.
[27]
Yaqi Liu, Binbin Lv, Xin Jin, Xiaoyu Chen and Xiaokun Zhang. 2023. TBFormer: Two-Branch Transformer for Image Forgery Localization. in IEEE Signal Processing Letters, vol. 30, pp. 623-627, 2023, https://doi.org/10.1109/LSP.2023.3279018.
[28]
Adam Paszke, Sam Gross, Francisco Massa, 2019. Pytorch: An imperative style, high-performance deep learning library. J. Advances in neural information processing systems, 32.
[29]
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell and Saining Xie, 2022. A ConvNet for the 2020s. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 11966-11976, https://doi.org/10.1109/CVPR52688.2022.01167.

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    ICIGP '24: Proceedings of the 2024 7th International Conference on Image and Graphics Processing
    January 2024
    480 pages
    ISBN:9798400716720
    DOI:10.1145/3647649
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    Published: 03 May 2024

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    Author Tags

    1. ConvNeXt
    2. Hierarchical-feature fusion
    3. Image forgery localization
    4. Two-branch

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