Digital image splicing passive detection method based on frequency domain local statistic model

A technology of local statistics and digital images, applied in image enhancement, image data processing, graphics and image conversion, etc., can solve the problems of low precision of digital image mosaic detection, inability to reflect edge details well, effectiveness enhancement, etc., to achieve The effect of solving the blind detection problem of image stitching, detection accuracy and computational efficiency advantages

Inactive Publication Date: 2015-09-09
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

However, the histogram feature based on the spatial domain LTP feature modeling method has a large correlation with the image content, which cannot well reflect the edge detail information generated in the...

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  • Digital image splicing passive detection method based on frequency domain local statistic model
  • Digital image splicing passive detection method based on frequency domain local statistic model
  • Digital image splicing passive detection method based on frequency domain local statistic model

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Embodiment Construction

[0053] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific implementation examples.

[0054] The present invention uses the image mosaic detection and evaluation database (CISDED) established by Columbia University as a reference database, which has been widely used in the field of image mosaic detection, including 933 real images and 912 mosaic images, and the image size is 128×128. The data type is in BMP format, available through http: / / www.ee.columbia.edu / ln / dvmm / downloads / AuthSplicedDataSet / AuthSplicedDataSet.htm. The SVM classifier in the present invention selects the LIBSVM software package developed by the team of Professor Lin Zhiren of National Taiwan University, which can be downloaded through http: / / www.csie.ntu.edu.tw / ~cjlin / libsvm / , wherein the SVM classifier Kernel function selection Radial Basis Kernel Function (RBF). The detailed implementation and specific operation pro...

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Abstract

The invention relates to a digital image splicing passive detection method based on a frequency domain local statistic model. The method comprises steps of firstly extracting integrated frequency domain LTP characteristics of each image and performing dimension reduction processing for standardized characteristics via kernel principal component analysis (KPCA); secondly, inputting data of the characteristics going through the dimension reduction processing into an SVM classifier for training so as to obtain a trained classifier; and at last, performing standardization and dimension reduction processing for the frequency domain LTP characteristics of to-be-measured images according to the above method and performing identification for authenticity of the to-be-measured images by use of the trained SVM classifier. The method is remarkably advantaged by detection precision and calculation efficiency.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to a digital image splicing passive detection method based on a local statistical model in the frequency domain. Background technique [0002] With the rapid development of electronic information technology, digital images are widely used in scientific research, news reports, advertising planning and other fields due to their advantages of large amount of information and strong visibility. In daily life and work, most people edit and retouch digital images only to meet certain practical needs, but there are also some criminals who have some malicious purpose to forge and distribute some digitally forged images that are difficult to distinguish with the naked eye, thus Misleading the public and causing serious adverse effects on individuals and society. Therefore, digital image authenticity identification is of great significance in the fields of judicial identifica...

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Application Information

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IPC IPC(8): G06T5/50G06T3/40G06K9/66
Inventor 李生红张玉金张爱新王凡铭
Owner SHANGHAI JIAO TONG UNIV
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