基于边界不确定性学习的图像篡改定位方法

doi:10.13229/j.cnki.jdxbgxb.20250014

Abstract

Abstract:

The limitations of current image manipulation localization methods， such as the extraction of features at a single scale， the misdetection and omission of small tampered regions caused by background confusion， and the uncertainty existing in prediction results， are addressed. An image manipulation localization method based on edge uncertainty learning is proposed. The base features of the tampered image are extracted by means of a pyramid vision transformer. A coarse localization map is then generated through multi-level interactive coarse localization branches. To enhance the detection of small tampered regions， a small target-aware refinement branch is employed. Multi-scale feature fusion is achieved with the use of a dedicated module， which enables the full interaction and integration of features across different scales. Additionally， entropy-based perceptual loss is introduced to supervise boundary uncertainty， thus significantly reducing the uncertainty of the prediction results. The proposed method is evaluated on five widely-used public image tampering datasets in both in-domain and cross-domain experiments. It is demonstrated by the results that the method can effectively localize tampered regions and outperform existing approaches.

Key words: computer application, image manipulation localization, multi-scale feature interaction, refined perception of small tampering areas, boundary uncertainty learning

CLC Number:

TP391

Hai-peng CHEN,Hong-xin LIU,Hui KANG,Xue-jie LIU. Image manipulation localization method based on boundary uncertainty learning[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(12): 4063-4071.

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References 24

[1]	钟辉, 康恒, 吕颖达, 等. 基于注意力卷积神经网络的图像篡改定位算法[J]. 吉林大学学报: 工学版, 2021, 51(5): 1838-1844.
	Zhong Hui, Kang Heng, Ying-da Lyu, et al. Image manipulation localization algorithm based on channel attention convolutional neural networks[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(5): 1838-1844.
[2]	Shi Z, Chen H, Zhang D. Transformer-auxiliary neural networks for image manipulation localization by operator inductions[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(9): 4907-4920.
[3]	石泽男, 陈海鹏, 张冬, 等. 预训练驱动的多模态边界感知视觉 Transformer[J]. 软件学报, 2023, 34(5): 2051-2067.
	Shi Ze-nan, Chen Hai-peng, Zhang Dong, et al. Pretraining-driven multimodal boundary-aware vision transformer[J]. Journal of Software, 2023, 34(5): 2051-2067.
[4]	Liu Y, Zhu X, Zhao X, et al. Adversarial learning for constrained image splicing detection and localization based on atrous convolution[J]. IEEE Transactions on Information Forensics and Security, 2019, 14(10): 2551-2566.
[5]	Chen B, Tan W, Coatrieux G, et al. A serial image copy-move forgery localization scheme with source/target distinguishment[J]. IEEE Transactions on Multimedia, 2020, 23: 3506-3517.
[6]	Zhang Y, Fu Z, Qi S, et al. Localization of inpainting forgery with feature enhancement network[J]. IEEE Transactions on Big Data, 2022, 9(3): 936-948.
[7]	Popescu A C, Farid H. Exposing digital forgeries in color filter array interpolated images[J]. IEEE Transactions on Signal Processing, 2005, 53(10): 3948-3959.
[8]	Bianchi T, Piva A. Image forgery localization via block-grained analysis of JPEG artifacts[J]. IEEE Transactions on Information Forensics and Security, 2012, 7(3): 1003-1017.
[9]	Mahdian B, Saic S. Using noise inconsistencies for blind image forensics[J]. Image and Vision Computing, 2009, 27(10): 1497-1503.
[10]	Chen X, Dong C, Ji J, et al. Image manipulation detection by multi-view multi-scale supervision[C]//Proceedings of the IEEE/CVF. International Conference on Computer Vision. Montreal, QC, Canada, 2021: 14165-14173.
[11]	Shi C, Wang C, Zhou X, et al. DAE-net: Dual attention mechanism and edge supervision network for image manipulation detection and localization[J]. IEEE Transactions on Instrumentation and Measurement, 2024(73): No.5028112.
[12]	Wang W, Xie E, Li X, et al. Pvt v2: Improved baselines with pyramid vision transformer[J]. Computational Visual Media, 2022, 8(3): 415-424.
[13]	Park T, Liu M Y, Wang T C, et al. Semantic image synthesis with spatially-adaptive normalization[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA: USA, 2019: 2337-2346.
[14]	Pang Y, Zhao X, Xiang T Z, et al. Zoom in and out: A mixed-scale triplet network for camouflaged object detection[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans, LA: USA, 2022: 2160-2170.
[15]	Dong J, Wang W, Tan T. Casia image tampering detection evaluation database[C]∥2013 IEEE. China Summit and International Conference on Signal and Information Processing.Beijing: China, 2013: 422-426.
[16]	Guan H, Kozak M, Robertson E, et al. MFC datasets: Large-scale benchmark datasets for media for ensic challenge evaluation[C]∥2019 IEEE.Winter Applications of Computer Vision Workshops (WACVW). Waikoloa, HI: USA, 2019: 63-72.
[17]	Wen B, Zhu Y, Subramanian R, et al. COVERAGE—a novel database for copy-move forgery detection[C]∥2016 IEEE. International Conference on Image Processing (ICIP).Phoenix, AZ: USA, 2016: 161-165.
[18]	Hsu Y F, Chang S F. Detecting image splicing usinggeometry invariants and camera characteristics consistency[C]∥2006 IEEE. International Conference on Multimedia and Expo. Toronto, ON: Canada, 2006: 549-552.
[19]	Novozamsky A, Mahdian B, Saic S. IMD2020 a largescale annotated dataset tailored for detecting manipulated images[C]∥Proceedings of the IEEE/CVF. Winter Conference on Applications of Computer Vision Workshops. Snowmass Village, CO: USA, 2020: 71-80.
[20]	Wu Y, AbdAlmageed W, Natarajan P. Mantra-net manipulation tracing network for detection and localization of image forgeries with anomalous features[C]∥Proceedings of the IEEE/CVF. Conference on Computer Vision and Pattern Recognition. Long Beach, CA: USA, 2019: 9543-9552.
[21]	Hu X, Zhang Z, Jiang Z, et al. SPAN spatial pyramid attention network for image manipulation localization[C]∥Proceedings of the European Conference on Computer Vision (ECCV). Glasgow: UK, 2020: 312-328.
[22]	Zhuang P, Li H, Tan S, et al. Image tampering localization using a dense fully convolutional network[J]. IEEE Transactions on Information Forensics and Security, 2021, 16: 2986-2999.
[23]	Zhuo L, Tan S, Li B, et al. Self-adversarial training incorporating forgery attention for image forgery localization[J]. IEEE Transactions on Information Forensics and Security, 2022, 17: 819-834.
[24]	Xia X, Su L C, Wang S P, et al. DMFF-net: Double-stream multilevel feature fusion network for image forgery localization[J]. Engineering Applications of Artificial Intelligence, 2024, 127: No.107200.

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数据集	分割设置		篡改操作类型			后处理操作
数据集	训练	测试	拼接	复制-粘贴	移除	后处理操作
CASIA^［15］	5 123	921	√	√		√
NIST16^［16］	404	160	√	√	√	√
COVER^［17］	75	25		√		√
Columbia^［18］	130	50	√
IMD2020^［19］	1 610	400	√	√	√	√

方法	CASIAV		NIST16		Columbia		COVER		IMD2020		Mean
方法	AUC	F₁	AUC	F₁	AUC	F₁	AUC	F₁	AUC	F₁	AUC	F₁
ManTra-Net^［20］	0.648	0.223	0.795	0.462	0.824	—	0.777	0.283	0.785	0.265	0.766	0.308
SPAN^［21］	0.709	0.213	0.961	0.582	0.936	0.815	0.791	0.325	—	—	0.849	0.484
DenseFCN^［22］	0.631	0.209	0.954	0.704	0.881	0.710	0.754	0.185	0.723	0.286	0.789	0.419
SATFL^［23］	0.697	0.246	0.937	0.613	0.892	0.804	0.767	0.347	0.796	0.300	0.818	0.462
MVSS-Net^［10］	0.748	0.390	0.981	0.827	0.719	0.703	0.808	0.284	0.817	0.411	0.815	0.523
TANet^［2］	0.739	0.493	0.955	0.901	0.982	0.960	0.756	0.425	0.766	0.383	0.840	0.633
DMFF-Net^［24］	0.791	0.386	0.976	0.843	0.945	0.837	0.727	0.282	0.784	0.328	0.845	0.535
DAE-Net^［11］	0.805	0.494	0.984	0.871	0.973	0.872	0.741	0.330	0.826	0.338	0.866	0.581
PBUL-Net	0.852	0.662	0.981	0.906	0.982	0.961	0.819	0.525	0.796	0.488	0.886	0.708

方法	CASIAV		NIST16		Columbia
方法	AUC	F₁	AUC	F₁	AUC	F₁
ManTra-Net^［21］	0.648	0.223	0.475	0.095	0.481	0.386
DenseFCN^［22］	0.631	0.209	0.604	0.051	0.586	0.317
MVSS-Net^［23］	0.748	0.390	0.643	0.246	0.697	0.471
TANet^［2］	0.739	0.493	0.638	0.248	0.710	0.440
DAE-Net^［12］	0.805	0.494	0.663	0.299	0.726	0.486
PBUL-Net	0.852	0.662	0.702	0.344	0.800	0.690

网络	组件					AUC	F₁
网络	MFFM	X	FAM	FAEM	EUBAL	AUC	F₁
a						0.810	0.605
b	√					0.819	0.618
c	√	√				0.832	0.629
d	√	√	√			0.838	0.641
e	√	√	√	√		0.848	0.653
f	√	√	√	√	√	0.852	0.662

Image manipulation localization method based on boundary uncertainty learning

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