Image Inpainting Forensics Algorithm Based on Deep Neural Network

Fig. 1. Image inpainting process by Criminisi algorithm. (a) Determining the inpainting block; (b) determining the matching block; (c) inpainting the image block; (d) renewal the margin

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Fig. 2. Image inpainting forensics. (a) Original image; (b) falsified image; (c) image after CNN inpainting forensics

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Fig. 3. Structural diagram of network

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Fig. 4. Structural diagram of network

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Fig. 5. Partial classical images in testing set

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Fig. 6. Falsified images with different area ratios of falsification regions. (a) 5%; (b) 10%; (c) 20%; (d) 0-5%; (e) 10%-30%; (f) 30%-50%

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Fig. 7. Detection results of inpainting regions. (a) Original image; (b) mask image; (c) inpainted image; (d) detection results by algorithm in Ref. [14]; (e) detection results by algorithm in Ref. [15]; (f) detection results by proposed method

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Table 1. Structural parameters of convolution-wide network
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Table 1. Structural parameters of convolution-wide network
Type of network Encoder network Decoder network
Number offeature maps 64 128 256 512 512 256 256 128 64 48
Feature size 256×256 128×128 64×64 32×32 16×16 16×16 32×32 64×64 128×128 256×256

Table 2. TPR and FPR of classical images by different algorithms (unit: %)

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Table 2. TPR and FPR of classical images by different algorithms (unit: %)

Image No.	Method in Ref. [14]		Method in Ref. [15]		Proposed method
Image No.	TPR	FPR	TPR	FPR	TPR	FPR
Fig. 5(a)	66.55	1.24	88.53	0.2	95.03	4.87
Fig. 5(b)	90.42	4.13	85.01	0	98.03	0.16
Fig. 5(c)	86.04	30.09	83.15	0.86	96.78	3.22
Fig. 5(d)	92.42	6.75	92.16	0.27	98.99	0.09
Fig. 5(e)	0	10.09	89.29	14.42	97.69	0.15

Table 3. TPR and FPR of images in UCID dataset by different algorithms (unit: %)
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Table 3. TPR and FPR of images in UCID dataset by different algorithms (unit: %)
Image Num. Method in Ref. [14] Method in Ref. [15] Proposed method
TPR FPR TPR FPR TPR FPR
200 87.35 14.7 94.75 10.8 95.6 0.8

Table 4. Detection results of images inpainted by Criminisi algorithm
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Table 4. Detection results of images inpainted by Criminisi algorithm
Parameter Mask size /% TPR /% FPR /% T /s
Regularregion 5 98.64 0.34 1.8
10 97.84 0.24 1.9
20 96.90 0.15 1.8
Irregularregion 0-10 90.43 0.4 2.1
10-30 94.88 1.08 2.0
30-50 96.96 1.74 1.8
Mean — 95.94 0.674 1.9

Table 5. Detection results of images inpainted by Shift-map algorithm
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Table 5. Detection results of images inpainted by Shift-map algorithm
Parameter Mask size /% TPR /% FPR /% T /s
Regularregion 5 93.29 0.29 1.9
10 92.48 0.19 1.8
20 89.95 0.25 2
Irregularregion 0-10 80.09 0.32 2.1
10-30 80.36 2.18 2.2
30-50 73.79 6.17 2.0
Mean — 84.99 1.57 2

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Xinshan Zhu, Yongjun Qian, Biao Sun, Chao Ren, Ya Sun, Siru Yao. Image Inpainting Forensics Algorithm Based on Deep Neural Network[J]. Acta Optica Sinica, 2018, 38(11): 1110005

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