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Depth forgery detection method based on frequency domain filtering residual error

A technology for forgery detection and frequency domain filtering, applied in image analysis, image enhancement, instrumentation, etc., can solve the problems of low quality and affect the performance of existing methods, and achieve the effect of good robustness and improved accuracy

Pending Publication Date: 2022-07-15
DALIAN UNIV OF TECH
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Problems solved by technology

However, in real-world applications, the quality of images and videos may be relatively low after various post-processing operations similar to compression, which greatly affects the performance of existing methods.

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  • Depth forgery detection method based on frequency domain filtering residual error
  • Depth forgery detection method based on frequency domain filtering residual error
  • Depth forgery detection method based on frequency domain filtering residual error

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Embodiment Construction

[0021] A deep forgery detection method based on frequency domain filtering residual, the technical scheme is as follows: including a preprocessing module and a classification module, such as: figure 1 shown, including the following steps:

[0022] S1. In the preprocessing module, the low-frequency information map of the image is obtained by performing Haar wavelet transform on the image;

[0023] S2. In the preprocessing module, the grayscale image and the low-frequency information image of the original image are subjected to residual operation to obtain the residual image of medium and high frequency information of the original image;

[0024] S3. In the classification module, the original image and the medium and high frequency information residual image are spliced ​​and input into the convolutional neural network for classification processing.

[0025] Wherein, in the described step S1, the RGB image I O Grayscale to get the grayscale image I of the original image G , f...

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Abstract

A depth forgery detection method based on a frequency domain filtering residual error belongs to the technical field of image classification, comprises a preprocessing module and a classification module, and comprises the following steps: performing Haar wavelet transform on an image to obtain a low-frequency information graph of the image; performing residual operation on the grey-scale image and the low-frequency information image of the original image to obtain a medium-high frequency information residual image of the original image; in the classification module, the original image and the medium-high frequency information residual image are spliced and then input into a convolutional neural network for classification processing. The features of the medium-high frequency domain of the image are obtained through Haar wavelet transform and the residual error, so that weakened or polluted counterfeit traces in the RGB domain caused by image compression are mined in the frequency domain, and the precision of the detection method in detecting the compressed image is improved. The RGB image and the high-frequency residual image are spliced together to serve as input of the convolutional neural network, and rich semantic information of the RGB domain and detail texture information of the medium-high frequency domain are fully utilized, so that the detection method has good robustness for image compression.

Description

technical field [0001] The invention belongs to the technical field of image classification, and in particular relates to a deep forgery detection method based on frequency domain filtering residuals. Background technique [0002] In recent years, with the development of science and technology, social media and short video platforms have risen rapidly, occupying most of people's entertainment time. More and more people like to share images and videos of their daily life on various social media and short video platforms. These images and videos will spread rapidly on the Internet and become the main carrier for people to communicate and share information with each other. However, image tampering technology, especially face image tampering technology, brings certain negative effects to the dissemination of images and videos. The tampered face images and videos may be used for improper purposes. Therefore, the detection of tampered face images and videos has great practical s...

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Application Information

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IPC IPC(8): G06T7/00G06T5/50G06T5/10G06N3/04G06K9/62G06V10/764G06V10/82
CPCG06T7/0002G06T5/10G06T5/50G06T2207/10016G06T2207/20084G06N3/045G06F18/24
Inventor 李育才王波宋增人
Owner DALIAN UNIV OF TECH