Image authenticity identification method and application thereof in certificate identification

An image, authenticity technology, applied in image enhancement, image analysis, image data processing, etc., can solve problems such as deformation, falsified area boundary feature destruction, etc., and achieve the effect of improving accuracy

Active Publication Date: 2020-07-24
JIANGNAN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

If the input of the network is the entire image, the image will be stretched and deformed to a un

Method used

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  • Image authenticity identification method and application thereof in certificate identification
  • Image authenticity identification method and application thereof in certificate identification
  • Image authenticity identification method and application thereof in certificate identification

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0084] The following will introduce our model training process. First, we will introduce the processing process of the data set CASIAv2.0 used for model training, and then introduce the hyperparameters and specific steps we use in the training process.

[0085] Processing of datasets

[0086] The application of deep learning in the field of image forensics is not very extensive. In the field of image forensics, there is no data set containing millions of pictures like ImageNet. The largest data set in the field of image forensics is CASIA v2.0[Dong J, Wang W, Tan T.Casia image tampering detection evaluation database[C] / / 2013IEEE China Summit and International Conference on Signal and Information Processing.IEEE,2013:422 -426.], this data set consists of 7491 original pictures and 5123 tampered pictures. The pictures in the data set have various shooting scenes and rich types of objects. The tampered pictures in CASIA v2.0 are all synthesized by Adobe Photoshop CS3 software. T...

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Abstract

The invention discloses an image authenticity identification method and application thereof in certificate identification, and belongs to the field of digital image processing. With the development and application of digital image editing software, lawbreakers can edit images such as identity cards, business licenses and business licenses illegally, and a adulterate effect is achieved. Therefore,the demand of image evidence obtaining becomes increasingly urgent, and most of existing models can only detect whether images are tampered or not and cannot well segment tampered areas. The inventionprovides an image tampering detection method based on a convolutional neural network, which can effectively segment a tampered area in an image by fusing multiple features of the tampered image. Experiments are carried out on a plurality of data sets, and experiment results further show that our work is effective.

Description

technical field [0001] The invention provides a method for identifying the authenticity of pictures based on deep learning, which belongs to the field of digital image processing. Background technique [0002] With the development of science and technology, digital images have been widely used in our lives, and even in court, images and videos are also very important sources of evidence. However, while digital images are convenient for users, they also give some lawbreakers an opportunity to take advantage of. Without authorization, lawbreakers carry out illegal operations on image content, such as editing in violation of regulations and synthesizing false images, etc., resulting in flooding of tampered images in people's social life. The most common tampering methods of digital images mainly include the following three: image splicing, image copy and paste, and partial image removal. Such as figure 1 As shown, image stitching refers to the addition of objects from other ...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08G06T7/11
CPCG06T7/001G06T7/11G06N3/08G06T2207/10024G06T2207/20084G06N3/045G06F18/253
Inventor 孙俊朱昊昱吴豪吴小俊方伟陈祺东李超游琪冒钟杰
Owner JIANGNAN UNIV
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