Local fuzzy interpolation blind detection method based on images

A blind detection and edge detection algorithm technology, applied in image analysis, image data processing, computer parts and other directions, can solve the problems of discounted detection effect, poor detection accuracy, poor effect, etc.

Inactive Publication Date: 2010-10-13
SHANGHAI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

(2) Image edge blur detection problem
(3) Synthesis of reduced-resolution images
It is less effective when detecting images outside the im...

Method used

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  • Local fuzzy interpolation blind detection method based on images
  • Local fuzzy interpolation blind detection method based on images
  • Local fuzzy interpolation blind detection method based on images

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Embodiment Construction

[0048] Embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0049] The image-based local fuzzy tampering blind detection method of the present invention, such as figure 1 As shown, the specific steps are as follows:

[0050] (1), image preprocessing

[0051](1-1), carry out low-pass filtering to whole image, eliminate the impact of noise on detection result;

[0052] (1-2), using the homomorphic filter of the wavelet domain to carry out the homomorphic filtering of the image in the wavelet domain, such as image 3 As shown in the figure, WT represents n-level wavelet transform, IWT represents n-level inverse wavelet transform, EXP represents exponent operation, LF represents a high-pass filter,

[0053] The specific steps are as follows:

[0054] (1-2-1), at first image is carried out to take logarithm successively, wavelet transform and high-pass filter, the functional formula of high-pass filter i...

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Abstract

The invention discloses a local fuzzy interpolation blind detection method based on images, comprising the following steps of: (1) preprocessing images; (2) calibrating the edges of the images by respectively using a prewitt edge detection arithmetic and a polynomial fitting edge detection arithmetic; (3) comparing the image edges calibrated by the two arithmetics to determine suspicious fuzzy interpolation edges; (4) eliminating fault detection points; and (5) calibrating the local fuzzy interpolation areas of the images. In the method, the two different edge detection arithmetics are compared, and the strong image edges which have greater influence on detection results are removed when the image edges are compared; the arithmetics of the method carry out filtering and edge enhancement on the images before detection, which can improve the capacity of the arithmetics for resisting the postprocessing of the images; after different degrees of noise increase processing and JPEG (Joint Photographic Experts Group) compression processing are carried out on the images, the images still have high accuracy on different types of fuzzy interpolations.

Description

technical field [0001] The invention relates to an image-based local fuzzy tampering blind detection method. This method is aimed at man-made splicing of different images and tampering processing of splicing edges by means of local blurring in order to eliminate splicing traces. This kind of local blur tampering image is common in real life. This method has broad application prospects in the field of image information security. Background technique [0002] With the development of digital technology, images, as a common way of expressing information, are widely used in all aspects of society. Images are increasingly stored in digital form. The popularization of digital capture equipment and the diversification of image editing software functions make it easy for people to manipulate digital images, and some of them maliciously tamper with images that will bring negative impacts on the lives of others. Image stitching and blurring are common methods in digital image proce...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06T7/00
Inventor 何超方勇
Owner SHANGHAI UNIV
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