Abstract：To address the insufficient utilization of edge information and high-frequency features in image tampering detection under complex scenarios, an image tampering detection network based on edge information and contrastive learning(EICL-Net) is proposed. First, a dynamic weight update strategy is designed to enhance the feature extraction capability for high-frequency image information. Next, by integrating edge detection algorithms with tampered region detection algorithms, the edge features of images are extracted and enhanced, and the saliency of anomalous information is improved. Finally, a contrastive learning mechanism is introduced to optimize the ability to distinguish pixel distribution differences by constructing positive and negative sample pairs for feature comparison, thereby achieving precise localization of tampered regions. Experiments on multiple public datasets demonstrate that EICL-Net exhibits strong generalization performance and the ability to identify subtle tampering traces under complex scenarios. Therefore, EICL-Net offers a solution to image tampering detection. With its high practical application value, EICL-Net can be widely applied in the fields such as information security and digital forensics.
王轶群, 高燕程. 基于边界信息与对比学习的图像篡改检测网络[J]. 模式识别与人工智能, 2025, 38(7): 655-667.
WANG Yiqun, GAO Yancheng. Image Tampering Detection Network Based on Edge Information and Contrastive Learning. Pattern Recognition and Artificial Intelligence, 2025, 38(7): 655-667.
[1] KAUR G, SINGH N, KUMAR M. Image Forgery Techniques: A Review. Artificial Intelligence Review, 2023, 56(2): 1577-1625. [2] NABI S T, KUMAR M, SINGH P, et al. A Comprehensive Survey of Image and Video Forgery Techniques Variants, Challenges, and Future Directions. Multimedia Systems, 2022, 28(3): 939-992. [3] KUMAR C, SINGH A K, KUMAR P. A Recent Survey on Image Watermarking Techniques and Its Application in E-Governance. Multimedia Tools and Applications, 2018, 77: 3597-3622. [4] LAWGALY A, KHELIFI F, BOURIDANE A. Image Sharpening for Efficient Source Camera Identification Based on Sensor Pattern Noise Estimation // Proc of the 4th International Conference on Emerging Security Technologies. Washington, USA: IEEE, 2013: 113-116. [5] SWAMINATHAN A, WU M, LIU K J R. Nonintrusive Component Forensics of Visual Sensors Using Output Images. IEEE Transactions on Information Forensics and Security, 2007, 2(1): 91-106. [6] LI B, SHI Y Q, HUANG J W. Detecting Doubly Compressed JPEG Images by Using Mode-Based First Digit Features // Proc of the IEEE 10th Workshop on Multimedia Signal Processing. Washington, USA: IEEE, 2008: 730-735. [7] KIRCHNER M, FRIDRICH J. On Detection of Median Filtering in Digital Images. SPIE, 2010, 7541. DOI: 10.1117/12.839100. [8] REN R Y, HAO Q X, NIU S Z, et al. MFI-Net: Multi-feature Fusion Identification Networks for Artificial Intelligence Manipulation. IEEE Transactions on Circuits and Systems for Video Technology, 2024, 34(2): 1266-1280. [9] LIU X H, LIU Y J, CHEN J, et al. PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(11): 7505-7517. [10] ZHOU P, HAN X T, MORARIU V I, et al. Learning Rich Features for Image Manipulation Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 1053-1061. [11] CHEN X K, DONG C B, JI J Q, et al. Image Manipulation Detection by Multi-view Multi-scale Supervision // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2021: 14165-14173. [12] SHI Z N, CHEN H P, ZHANG D. Transformer-Auxiliary Neural Networks for Image Manipulation Localization by Operator Inductions. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(9): 4907-4920. [13] LIN X, WANG S, DENG J H, et al. Image Manipulation Detection by Multiple Tampering Traces and Edge Artifact Enhancement. Pattern Recognition, 2023. DOI: 10.1016/j.patcog.2022.109026. [14] MAHDIAN B, SAIC S. Using Noise Inconsistencies for Blind Image Forensics. Image and Vision Computing, 2009, 27(10): 1497-1503. [15] FERRARA P, BIANCHI T, DE ROSA A, et al. Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts. IEEE Transactions on Information Forensics and Security, 2012, 7(5): 1566-1577. [16] KRAWETZ N, SOLUTIONS H F. A Picture's Worth[C/OL]. [2025-03-29]. https://www.blackhat.com/presentations/bh-usa-07/Krawetz/Whitepaper/bh-usa-07-krawetz-WP.pdf [17] FRIDRICH J, KODOVSKÝ J. Rich Models for Steganalysis of Digital Images. IEEE Transactions on Information Forensics and Security, 2012, 7(3): 868-882. [18] GUILLARO F, COZZOLINO D, SUD A, et al. TruFor: Leve-raging All-Round Clues for Trustworthy Image Forgery Detection and Localization// Proc of the IEEE/CVF Conference on Compu- ter Vision and Pattern Recognition. Washington, USA: IEEE, 2023: 20606-20615. [19] WANG X D, ZHOU J R, ZHOU H Y, et al. HRGR: Enhancing Image Manipulation Detection via Hierarchical Region-Aware Graph Reasoning[C/OL].[2025-03-29]. https://arxiv.org/pdf/2410.21861. [20] HU X F, ZHANG Z H, JIANG Z Y, et al. SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization // Proc of the 16th European Conference on Computer Vision. Berlin, Germany: Springer, 2020: 312-328. [21] SHI Z N, CHANG C Q, CHEN H P, et al. PR-NET: Progressively-Refined Neural Network for Image Manipulation Localization. International Journal of Intelligent Systems, 2022, 37(5): 3166-3188. [22] DING H W, CHEN L Y, TAO Q, et al. DCU-Net: A Dual-Cha-nnel U-Shaped Network for Image Splicing Forgery Detection. Neural Computing and Applications, 2023, 35(7): 5015-5031. [23] WU Z R, XIONG Y J, YU S X, et al. Unsupervised Feature Lear-Unsupervised Feature Lear-ning via Non-parametric Instance Discrimination // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 3733-3742. [24] CHEN T, KORNBLITH S, NOROUZI M, et al. A Simple Framework for Contrastive Learning of Visual Representations[C/OL].[2025-03-29]. https://arxiv.org/pdf/2002.05709/1000. [25] CHEN X L, HE K M. Exploring Simple Siamese Representation Learning // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 15745-15753. [26] WU H W, CHEN Y M, ZHOU J T. Rethinking Image Forgery Detection via Soft Contrastive Learning and Unsupervised Clustering[C/OL]. [2025-03-29].https://arxiv.org/pdf/2308.09307. [27] ZENG Y Y, ZHAO B W, QIU S Z, et al. Toward Effective Image Manipulation Detection with Proposal Contrastive Learning. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(9): 4703-4714. [28] DONG J, WANG W, TAN T N. CASIA Image Tampering Detection Evaluation Database // Proc of the IEEE China Summit and International Conference on Signal and Information Processing. Washington, USA: IEEE, 2013: 422-426. [29] HSU Y F, CHANG S F. Detecting Image Splicing Using Geometry Invariants and Camera Characteristics Consistency // Proc of the IEEE International Conference on Multimedia and Expo. Washington, USA: IEEE, 2006: 549-552. [30] WEN B H, ZHU Y, SUBRAMANIAN R, et al. COVERAGE-A Novel Database for Copy-Move Forgery Detection // Proc of the IEEE International Conference on Image Processing. Washington, USA: IEEE, 2016: 161-165.
2025, Vol. 38 
