Image stitching tampering detection method

An image mosaic and detection method technology, which is applied in the field of image analysis, can solve the problems of tampering targets in areas that are easy to be ignored, and the inability to accurately locate tampered areas in stitched images, etc.

Active Publication Date: 2020-04-28
HEBEI UNIV OF TECH
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

[0008] The technical problem to be solved by the present invention is to provide a detection method for image mosaic tampering, which is a detection method for image mosaic tampering based on the hybrid domain attention mechanism and the hollow space py...

Method used

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  • Image stitching tampering detection method
  • Image stitching tampering detection method

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Experimental program
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Embodiment

[0095] The specific steps of the detection method for image mosaic tampering based on the hybrid domain attention mechanism and the empty space pyramid pooling module in this embodiment are as follows:

[0096] In the first step, the depth feature map F of the input image is extracted:

[0097] Adjust the size of the input image I to 256×256 pixels, and extract the depth feature map F of the input image through the VGG16 deep neural network module, as shown in the following formula (1),

[0098] F=VGG16(Resize(I)) (1),

[0099] In formula (1), VGG16( ) is the VGG16 deep neural network module, and Resize( ) is a function to adjust the size of the input image;

[0100] The above VGG16 deep neural network module includes convolution, hole convolution, Relu, pooling operations,

[0101] The convolution operation is as follows formula (19),

[0102] F_out=(F_in+2pad-k_size) / stride+1 (19),

[0103] In formula (19), F_out is the result after the convolution layer, F_in is the inp...

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Abstract

The invention discloses an image stitching tampering detection method, relates to the field of image analysis, and is an image stitching tampering detection method based on a mixed domain attention mechanism and a cavity space pyramid pooling module, and the method comprises the steps of extracting a depth feature map F of an input image; obtaining a feature map Ffinal of the tampered area by adopting a mixed domain attention mechanism; adopting a cavity space pyramid pooling module to obtain a final positioning mask M; training a stitching tampering detection method based on the mixed domainattention mechanism and the cavity space pyramid pooling module. According to the measurement of image stitching tampering detection based on the mixed domain attention mechanism and the cavity spacepyramid pooling module, the defects that in the prior art, the tampering area of a stitched image cannot be accurately positioned based on a certain specific hypothesis, and a tampering target with asmall area is easily ignored in detection are overcome.

Description

technical field [0001] The technical solution of the present invention relates to the field of image analysis, in particular to a detection method for image splicing and tampering. Background technique [0002] With the rapid development of image editing software such as Photoshop, people can easily modify digital images according to their own wishes, reaching the level of false ones. Such forged images and pictures will distort the truth, cause misunderstandings to the public, and adversely affect the development of society. Therefore, it is necessary to detect forged and tampered images to protect the authenticity and integrity of digital images and avoid misleading, fraud and copyright disputes caused by tampered images. [0003] Stitching is the most common way of image tampering, which is to splicing an object or a certain area in one image into another image, so as to hide or increase an object or a certain area in the image. When stitching different images, post-pro...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T3/40
CPCG06T3/4038G06T7/0002G06T2207/10028G06T2207/20016G06T2207/20081G06T2207/20084G06T2207/20221
Inventor 阎刚陈超凡朱叶郭迎春刘依于洋郝小可于明
Owner HEBEI UNIV OF TECH
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