Digital image evidence obtaining method for detecting photo origin by covariance matrix

Abstract

The invention belongs to the technical field of signal and information processing, and relates to a digital-image evidence obtaining method for detecting camera sources of digital photo images. The method is characterized in that a CFA interpolation coefficient in an imaging process is estimated by utilizing a covariance matrix under the circumstance of only acquiring a digital image; an optimal feature is preferred by adopting an SFFS feature selection algorithm and is taken as the feature of classification detection; and through pre-trained models and parameters, the digital image with an unknown source is detected and the camera-source evidence of the digital image is obtained by using a support vector machine SVM as a classifier. The method has the advantages of accurately identifying the camera source of the digital image and obtaining evidence under the circumstance of only acquiring the digital image. The method is suitable for the field of information security.

Description

technical field [0001] The invention belongs to the technical field of signal and information processing, and relates to a digital image forensics method for detecting the source of a digital photo image camera. Background technique [0002] At present, there are three typical methods for detecting camera sources of digital photos. The first category is J. , J.Fridrich, M.Goljan in Digital "bullet scratches" for images method proposed in the article. They believe that there are unique features introduced by different cameras in digital photos, and they call it "digital ballistics". The pattern noise of images is extracted by wavelet denoising, which can be used as digital ballistics and used to detect digital image sources. However, this method must obtain a digital camera for capturing the image, which is often difficult to achieve in practice. The second type of method is represented by the algorithm proposed by M.Kharrazi, H.T.Sencar, and N.Memon in the Blind source c...

AI Technical Summary

Problems solved by technology

The second type of algorithm and the third type of algorithm are currently poor in identifying the source of cameras of the same brand and different models. At the same time, when the number of source cameras of the digital image to be forensic increases, the accuracy of these algorithms drops sharply, and often only 60% to 70% correct detection rate to obtain digital image camera source

But in reality, since the source of the image to be forensic is unknown, and there are many types of cameras in the market, these methods are less practical

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