A Median Filter Detection Method Based on Fluctuation Characteristics in Frequency Domain

Abstract

A median filter detection method based on frequency domain fluctuation characteristics: Discrete Fourier transform processing is performed on the acquired original image to obtain the frequency domain image of the original image; the frequency domain image is processed to obtain the frequency domain image that can reflect the original image. The description points of domain fluctuation characteristics, and the processed image is divided into N ring regions, and then the total number of description points of each ring region is counted, which is the constructed N-dimensional detection feature; the extracted N-dimensional detection The features are input into the support vector machine for training, and the median filter detector of the support vector machine is obtained, and then the median filter detector of the support vector machine is used to detect whether the original image has undergone median filtering. The present invention more intuitively reflects the difference between the image processed by the median filter and the original image, and can accurately detect whether the image has undergone the median filter operation under the premise of maintaining a low feature dimension and a simple algorithm process, and realizes high detection accuracy rate, it is robust.

Description

technical field [0001] The invention relates to a median filter detection method. In particular, it relates to a median filter detection method based on frequency domain fluctuation characteristics. Background technique [0002] Digital multimedia information has become the mainstream of today's media information production, processing, transmission, and storage. Digital images are also widely used in various fields such as entertainment, justice, business, military, and science because of their ease of processing and editing. With the popularization of terminals such as digital cameras, mobile phones and tablet computers, people can obtain various digital pictures anytime and anywhere. However, while the emergence of new technologies brings convenience to people, its negative effects are gradually emerging. Due to the easy acquisition and processing of digital information, some image processing technologies are used to maliciously tamper and forge images, destroying the a...

AI Technical Summary

Problems solved by technology

[0005] The above algorithms can effectively detect the median filter image, but the SPAM feature dimension is too large, and the calculation is time-consuming; although the MFF feature has a low dimension, the statistical process of constructing the feature is cumbersome and time-consuming; the MFR feature classification used The construction process of the autoregressive model and convolutional neural network is also more complicated

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