Image median filtering distinguishing method based on local binary pattern

Abstract

The invention discloses an image median filtering distinguishing method based on a local binary pattern. The method comprises the steps of extracting LBP features of a to-be-detected image I0 and a to-be-detected image II I '0; defining the value of the ratio calculation ratio of the number of the LBP features of the to-be-detected image I 0 to the number of the LBP features of the to-be-detectedimage II '0, and judging which of the to-be-detected image I0 and the to-be-detected image II' 0 is an original image and which is a median filtering version corresponding to the original image. Different from a general framework of existing image median filtering detection, the method does not depend on any statistical learning model, does not need any classifier or threshold, and is achieved bycalculating the number of specific LBP feature modes in a single image. According to the method, the defect that a learning model or a classifier is trained by using a large-scale sample database in the prior art is overcome, and the problems of subjectivity, one-sidedness and the like of manually setting a threshold value or obtaining the threshold value through an experiment are avoided; and themethod has high robustness for JPEG compression, rotation (at any angle), resampling, histogram equalization, noise addition and other operations.

Description

technical field [0001] The invention belongs to the technical field of image filter detection, and relates to an image median filter distinction method based on a local binary mode. Background technique [0002] Blind digital image forensics is an effective technology to protect the authenticity and integrity of image content. It refers to using the underlying characteristics of the image itself or statistical fingerprints to identify the authenticity and integrity of image content without prior information. However, content-preserving image processing operations can easily destroy such underlying features or statistical fingerprints. Nonlinear median filtering is one such operation that does not change the image content, but affects the effectiveness of a large number of forensic techniques, such as masking the traces of resampling and removing the statistical traces of blockiness caused by JPEG compression, which leads to JPEG Forensic technology fails, and so on. Theref...

AI Technical Summary

Problems solved by technology

Although [12] cannot be applied to images, it is indeed the first method that does not use a classifier model to distinguish median filtering

[0008] Throughout the current research of this type, there are generally the following problems: (1) most algorithms can only distinguish between median filtered images and unfiltered images, and cannot identify whether the image has undergone median filtering operations; (2) many existing median filtering The detection algorithm is a learning-based method, which treats the median filter detection problem as an image classification problem. This type of method requires a large-scale original image and median filter image as a training sample data set to train the classifier; (3) most of the existing Algorithms lack discussion of robustness

It aims to solve the disadvantages of existing technologies using large-scale sample databases to train learning models or classifiers, and avoid problems such as subjectivity and one-sidedness of artificially setting thresholds or obtaining thresholds through experiments

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