Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (4): 1320-1328.doi: 10.13229/j.cnki.jdxbgxb20170378

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Recaptured image forensics algorithm based on local plane linear point

Yan-jun SUN1,2,3(),Xuan-jing SHEN1,2,Hai-peng CHEN1,2,Yong-zhe ZHAO1,2()   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
    2. Symbol Computation and Knowledge Engineer，Ministry of Education, Jilin University, Changchun 130012, China
    3. Computer Department, Air Force Aviation University，Changchun 130022, China
  • Received:2017-04-17 Online:2019-07-01 Published:2019-07-16
  • Contact: Yong-zhe ZHAO E-mail:mka1982@163.com;yongzhe@jlu.edu.cn

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Abstract:

In order to solve the problem that the existing recaptured image algorithms have weak theoretical basis and low forensics rate, a new recaptured image identifying algorithm is put forward based on local plane linear point. Firstly, the proposed algorithm establishes the mathematical model in the imaging process, and provides concepts and properties of the local plane linear point from the model. Then the local plane linear point was extracted from image as the characteristic value. Finally the support vector machine is applied to classify the recaptured image with the characteristic value. The results show the proposed method can not only identify the recaptured image but also have better identification rate, and the dimension of the characteristic vector is also lower than those obtained by other algorithms.

Key words: computer application, information security, recaptured image, local plane linear point, image forensics

CLC Number: 

  • TP391

Fig.1

Visual contrast between real images andrecaptured images"

Fig.2

Imaging process of real image and recaptured image"

Fig.3

Mapping process of local planar linear points"

Fig.4

Comparison of local planar linear points mapping between real image and recaptured image"

Fig.5

Algorithm flow chart"

Table 1

Influence of different parameters on range of characteristic values"

参数 α E最大值
t=1.3 t=1.5 t=2.0
10-3 251.82 162.95 95.31
10-4 304.48 197.01 115.25
10-5 357.14 231.09 135.18
10-6 409.80 265.17 155.11
10-7 462.46 299.24 175.04
10-8 515.11 333.31 194.98
10-9 567.77 367.39 214.91
10-10 620.43 401.46 234.84
10-11 673.09 435.53 254.77
10-12 725.74 469.61 274.72

Fig.6

Fitting curve of parameters"

Fig.7

Distribution of local plane linear points in image"

Fig.8

Fourier transformation spectrum"

Fig.9

Three-dimensional power spectrum of image"

Fig.10

2D power spectrum curve"

Fig.11

Multiple images contrast of 2D power spectrum curve"

Table 2

Identification rate of algorithm under different parameters"

α t=1.3 t=1.5 t=2.0
ε 鉴别率/% ε 鉴别率/% ε 鉴别率/%
10-3 230 84.30 140 85.95 80 85.95
10-4 280 85.12 170 85.95 100 88.43
10-5 330 88.43 180 89.25 120 94.56
10-6 380 81.82 220 88.43 140 85.12
10-7 430 85.95 270 88.43 160 88.43

Table 3

Comparisons with previous works"

算法 维数 正确率/%
本文算法 120 94.56
文献[4] 192 85.25
文献[6] 166 84.28
文献[7] 136 88.57
文献[9] 64 98.25
文献[10] 216 78.49
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