Diffusion-Based Image Inpainting Forensics Via Gradient Domain Guided Filtering Enhancement

Tingting Liu; Yujin Zhang; Fei Wu; Shiting Xiong

doi:10.3788/LOP57.081003

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 8 >Page 081003 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 8, 081003 (2020)

Diffusion-Based Image Inpainting Forensics Via Gradient Domain Guided Filtering Enhancement

Tingting Liu¹, Yujin Zhang^1,2,*, Fei Wu¹, and Shiting Xiong¹

Author Affiliations

¹School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

²Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai Jiao Tong University, Shanghai 200240, China

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DOI: 10.3788/LOP57.081003 Cite this Article Set citation alerts

Tingting Liu, Yujin Zhang, Fei Wu, Shiting Xiong. Diffusion-Based Image Inpainting Forensics Via Gradient Domain Guided Filtering Enhancement[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081003 Copy Citation Text

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Fig. 1. Training and detection flow chart

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Fig. 2. Artifacts left after the image has been inpainted. (a) Original image; (b) ground truth; (c) δ_Δ_I map

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Fig. 3. Comparison of repair image positioning results. (a) Original image; (b) ground truth image; (c) location result obtained by algorithm used in reference [10]; (d) location result obtained by our algorithm

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Pixel size	Algorithm
	Square		Circular		Irregular
	Reference[10]	Proposed	Reference[10]	Proposed	Reference[10]	Proposed
64×64	0.8896	0.9088	0.8863	0.9056	0.8757	0.8947
32×32	0.7908	0.8192	0.7893	0.8176	0.7645	0.7913
16×16	0.6585	0.6880	0.6664	0.6960	0.6215	0.6489
8×8	0.4063	0.4381	0.4858	0.5174	0.1990	0.2137

Table 1. Comparison of F1 scores based on Isotropic repair algorithm

Pixel size	Algorithm
	Square		Circular		Irregular
	Reference[10]	Proposed	Reference[10]	Proposed	Reference[10]	Proposed
64×64	0.8821	0.9011	0.8790	0.8977	0.8661	0.8846
32×32	0.7808	0.8082	0.7757	0.8031	0.7480	0.7735
16×16	0.6465	0.6779	0.6442	0.6729	0.5940	0.6208
8×8	0.3171	0.3388	0.4065	0.4315	0.1726	0.1833

Table 2. Comparison of F1 score based on Edge-oriented repair algorithm

Pixel size	Algorithm
	Square		Circular		Irregular
	Reference[10]	Proposed	Reference[10]	Proposed	Reference[10]	Proposed
64×64	0.8147	0.8318	0.8610	0.8788	0.8297	0.8474
32×32	0.6189	0.6388	0.6953	0.7164	0.6768	0.6977
16×16	0.3138	0.3352	0.3184	0.3390	0.3833	0.4063
8×8	0.1007	0.1074	0.1234	0.1305	0.0784	0.0784

Table 3. Comparison of F1 scores based on Delaunay-oriented repair algorithm

Pixel size	Algorithm
	Square		Circular		Irregular
	Reference[10]	Proposed	Reference[10]	Proposed	Reference[10]	Proposed
64×64	0.8106	0.8336	0.8097	0.8335	0.7974	0.8194
32×32	0.6514	0.6828	0.6526	0.6852	0.6289	0.6602
16×16	0.4582	0.4915	0.4661	0.5007	0.4301	0.4626
8×8	0.2051	0.2217	0.2742	0.2982	0.1246	0.1373

Table 4. Comparison of F1 scores based on Isotropic repair algorithm (1.1×)

Pixel size	Algorithm
	Square		Circular		Irregular
	Reference[10]	Proposed	Reference[10]	Proposed	Reference[10]	Proposed
64×64	0.8001	0.8237	0.7981	0.8203	0.7850	0.8059
32×32	0.6351	0.6659	0.6342	0.6663	0.6078	0.6366
16×16	0.4447	0.4777	0.4427	0.4769	0.4079	0.4365
8×8	0.1582	0.1729	0.2222	0.2437	0.0972	0.1085

Table 5. Comparison of F1 score based on Edge-oriented repair algorithm (1.1×)

Pixel size	Algorithm
	Square		Circular		Irregular
	Reference[10]	Proposed	Reference[10]	Proposed	Reference[10]	Proposed
64×64	0.7374	0.7574	0.7687	0.7879	0.7466	0.7689
32×32	0.5033	0.5256	0.5425	0.5714	0.5398	0.5653
16×16	0.2311	0.2488	0.2280	0.2476	0.2588	0.2819
8×8	0.0450	0.0517	0.0603	0.0653	0.0436	0.0479

Table 6. Comparison of F1 scores based on Delaunay-oriented repair algorithm (1.1×)

Tingting Liu, Yujin Zhang, Fei Wu, Shiting Xiong. Diffusion-Based Image Inpainting Forensics Via Gradient Domain Guided Filtering Enhancement[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081003

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