Image Tampering Detection Method Based on Approximate Nearest Neighbor Search

Jing Wang; Yuchen Zhang; Zhanqiang Huo; Liqin Jia

doi:10.3788/LOP57.101102

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 10 >Page 101102 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 10, 101102 (2020)

Image Tampering Detection Method Based on Approximate Nearest Neighbor Search

Jing Wang, Yuchen Zhang, Zhanqiang Huo^*, and Liqin Jia

Author Affiliations

College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454003, China

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DOI: 10.3788/LOP57.101102 Cite this Article Set citation alerts

Jing Wang, Yuchen Zhang, Zhanqiang Huo, Liqin Jia. Image Tampering Detection Method Based on Approximate Nearest Neighbor Search[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101102 Copy Citation Text

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Fig. 1. Feature matching steps. (a) Initialization; (b) propagation; (c) random search

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Detection results of the algorithm on rotation transform operation. (a) Original image; (b) tampering image (rotation 90°); (c) matching result; (d) detection result

Fig. 2. Detection results of the algorithm on rotation transform operation. (a) Original image; (b) tampering image (rotation 90°); (c) matching result; (d) detection result

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Fig. 3. Detection results of the algorithm on scaling transformation operation. (a) Original image; (b) tampering image (reduced by 80%); (c) matching result; (d) detection result

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Fig. 4. Detection results of the algorithm on mirror transformation operation. (a) Original image; (b) mirror image (horizontal); (c) matching result; (d) detection result

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Fig. 5. Detection results of the algorithm for multi-region I transform operation. (a) Original image; (b) tampering image; (c) matching result; (d) detection result

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Fig. 6. Detection results of the algorithm for multi-region II transform operation. (a) Original image; (b) tampering image; (c) matching result; (d) detection result

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Fig. 7. Detection results of the algorithm for multi-region mirroring I transform operation. (a) Original image; (b) tampering image; (c) matching result; (d) detection result

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Fig. 8. Detection results of the algorithm for multi-region mirroring II transform operation. (a) Original image; (b) tampering image; (c) matching result; (d) detection result

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Parameter	Value	Explanation
T_d	50	Minimum distance of tampered area
τ	360	Minimum threshold of minimumsquare linear model
T_m	1000	Minimum radius of tampered area
ρ_m	5	Radius of median filter
ρ_e	8	Regional radius of minimumsquare linear model

Table 1. Experimental parameters

Algorithm	Rotate90°	Zoom80%	Multi-regionⅠ	Multi-region Ⅱ
Ref. [4]	81.34	50.14	54.15	62.12
Ref. [2]	79.17	84.23	79.31	84.13
Ref. [6]	85.54	83.54	91.23	92.15
This algorithm	94.76	90.45	93.75	93.12

Table 2. F-measure of algorithm detection under different tampering operations%

Algorithm	Rotate	Zoom	Multi-regionⅠ	Multi-regionⅡ
Ref. [4]	39.87	77.49	507.45	511.94
Ref. [2]	53.91	56.84	110.67	146.78
Ref. [6]	34.02	43.62	303.87	297.59
This algorithm	27.28	33.19	72.94	81.85

Table 3. Comparison of average running time of different algorithmss

Algorithm	F-measure		Time /s
Algorithm		Mirrorimage /%	Multi mirrorimage /%
Ref. [11]	90.62	53.46	140.43
Ref. [12]	84.43	72.90	165.72
Ref. [13]	91.12	75.12	179.32
This algorithm	94.49	93.52	80.97

Table 4. Comparison of tampering detection performance of different algorithms under mirror operation

Jing Wang, Yuchen Zhang, Zhanqiang Huo, Liqin Jia. Image Tampering Detection Method Based on Approximate Nearest Neighbor Search[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101102

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