Author Affiliations
1 School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China2 State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, Chinashow less
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
Fig. 2. Image inpainting forensics. (a) Original image; (b) falsified image; (c) image after CNN inpainting forensics
Fig. 3. Structural diagram of network
Fig. 4. Structural diagram of network
Fig. 5. Partial classical images in testing set
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%
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
Type of network | Encoder network | Decoder network |
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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 |
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Table 1. Structural parameters of convolution-wide network
Image No. | Method in Ref. [14] | Method in Ref. [15] | Proposed method |
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TPR | FPR | TPR | FPR | TPR | FPR |
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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 |
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Table 2. TPR and FPR of classical images by different algorithms (unit: %)
Image Num. | Method in Ref. [14] | Method in Ref. [15] | Proposed method |
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TPR | FPR | TPR | FPR | TPR | FPR |
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200 | 87.35 | 14.7 | 94.75 | 10.8 | 95.6 | 0.8 |
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Table 3. TPR and FPR of images in UCID dataset by different algorithms (unit: %)
Parameter | Mask size /% | TPR /% | FPR /% | T /s |
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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 |
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Table 4. Detection results of images inpainted by Criminisi algorithm
Parameter | Mask size /% | TPR /% | FPR /% | T /s |
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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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Table 5. Detection results of images inpainted by Shift-map algorithm