基于图像噪声残差的数字图像来源取证

doi:10.16180/j.cnki.issn1007-7820.2020.02.007

Abstract

Abstract:

There are some problems with the image source forensics algorithm using the support vector machine, such as the large training set (about thousands) and the high dimension feature. To solve these problems, a forensic algorithm was proposed, which required only a small number (about 10) of training images, and only extracted the residual of the image noise as the unique feature of the image. The algorithm firstly used the wavelet filter to extract the image noise, and then extracted the residual of the image noise using the regression model. Finally, the Gaussian distribution model was established for the noise residual, and the source evidence was obtained according to different types of images with different model parameters. The experimental results showed that under the condition of FPR of 1.2%, the TPR of the proposed algorithm for natural images was 95.33%, and the TPR for computer-generated graphics was 96.44%.

Key words: natural images, computer-generated graphics, image source forensics, the wavelet filter, regression model, gaussian model

CLC Number:

TP319

HUANG Mingying. Digital Image Source Forensics Based on Image Noise Residual[J].Electronic Science and Technology, 2020, 33(2): 37-42.

Figures/Tables 6

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References 17

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指标	本文算法	文献[2]	文献[11]	文献[8]	文献[7]
PNI	95.33%	98.78%	99.11%	56.90%	80.33%
PCG	96.44%	96.78%	97.89%	57.35%	73.44%

图像数量	0.1	0.2	0.3	0.4
n=10	97.00%	99.00%	99.56%	99.67%
n=20	97.11%	99.00%	99.56%	99.67%
n=30	97.22%	99.00%	99.56%	99.67%
n=40	97.22%	99.00%	99.56%	99.67%

Digital Image Source Forensics Based on Image Noise Residual

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