Source camera identification method and system based on edge-guided weighted average

A weighted average and edge-guided technology, applied in image data processing, instrumentation, computing, etc., can solve problems such as reducing the accuracy of source camera recognition results and interfering with camera fingerprint estimation, achieving fairness and effectiveness, suppressing artifacts, and reducing impact Effect

Pending Publication Date: 2020-11-17
UNIV OF JINAN
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The inventors of the present disclosure found that due to the imperfection of the current image denoising algorithm, a large number of structures related to the image content are left in the residual image. By comparing the difference between the original image and its residual image, it is possible to find the edge / Texture regions are highly correlated, smooth regions are good for camera fingerprint estimation, while texture / edge regions interfere with camera fingerprint estimation, thus reducing the result accuracy of source camera identification

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  • Source camera identification method and system based on edge-guided weighted average
  • Source camera identification method and system based on edge-guided weighted average
  • Source camera identification method and system based on edge-guided weighted average

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Embodiment 1

[0050] Such as figure 1 As shown, Embodiment 1 of the present disclosure provides a source camera identification method based on edge-guided weighted average, including the following steps:

[0051] Obtain image data to be recognized;

[0052] Obtain the image data captured by the camera;

[0053] Crop the acquired image data into image blocks of preset size;

[0054] Obtain the residual image of the image block, and construct an edge-weighted weight map of the residual image;

[0055] The obtained residual image and the corresponding edge weighted weight map are fused to estimate the camera fingerprint;

[0056]Calculate the weighted correlation value between the residual image of the image data to be recognized and the camera fingerprint, and perform source camera identification according to the weighted correlation value.

[0057] In detail, it includes edge-guided weighted average, maximum likelihood estimation residual fusion, weighted correlation and other parts, so ...

Embodiment 2

[0100] Embodiment 2 of the present disclosure provides a source camera identification system based on edge-guided weighted average, including:

[0101] The data acquisition module is configured to: acquire the image data captured by the camera;

[0102] The image cropping module is configured to: crop the acquired image data into image blocks of a preset size;

[0103] The weight assignment module is configured to: obtain a residual image of the image block, and construct an edge-weighted weight map of the residual image;

[0104] The fingerprint acquisition module is configured to: estimate the camera fingerprint after fusing the acquired residual image and the corresponding edge weighted weight map;

[0105] The identification module is configured to: calculate a weighted correlation value between the residual image of the image data to be identified and the camera fingerprint, and perform source camera identification according to the weighted correlation value.

[0106] T...

Embodiment 3

[0108] Embodiment 3 of the present disclosure provides a medium on which a program is stored. When the program is executed by a processor, the steps in the source camera identification method based on edge-guided weighted average as described in Embodiment 1 of the present disclosure are implemented. The steps are:

[0109] Obtain the image data captured by the camera;

[0110] Crop the acquired image data into image blocks of preset size;

[0111]Obtain the residual image of the image block, and construct an edge-weighted weight map of the residual image;

[0112] The obtained residual image and the corresponding edge weighted weight map are fused to estimate the camera fingerprint;

[0113] Calculate the weighted correlation value between the residual image of the image data to be recognized and the camera fingerprint, and perform source camera identification according to the weighted correlation value.

[0114] The detailed steps are the same as the edge-guided weighted ...

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Abstract

The invention provides a source camera identification method and system based on edge guide weighted average, and belongs to the technical field of source camera identification, and the method comprises the following steps: obtaining image data shot by a camera; cutting the acquired image data into image blocks with preset sizes; obtaining a residual image of the image block, and constructing an edge weighted weight graph of the residual image; fusing the obtained residual image and the corresponding edge weighted weight map, and estimating to obtain a camera fingerprint; calculating a weighted correlation value between the residual image of the to-be-identified image data and the camera fingerprint, and performing source camera identification according to the weighted correlation value; according to the method, different weights are distinguished through the given edge area and the non-edge area, so that the influence of the image edge area on the camera fingerprint is effectively reduced, the residual image is further fused on the statistical level through maximum likelihood estimation, and the source camera recognition effect is greatly improved.

Description

technical field [0001] The present disclosure relates to the technical field of source camera identification, in particular to a source camera identification method and system based on edge-guided weighted average. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] Digital images serve as an information carrier and can be used as effective evidence in court. However, as digital images are maliciously tampered with, people's trust in images decreases. Therefore, the subject of source camera identification in digital image forensics technology has received extensive attention. Sensor Pattern Noise (SPN) has been an effective solution to the SCI (source camera identification) problem because it is the unique fingerprint for identifying a specific device of the same brand and camera model. The current method of obtaining fingerprints is: giv...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/13G06T5/00
CPCG06T5/002G06T7/0002G06T2207/10004G06T7/13
Inventor 刘云霞张文娜
Owner UNIV OF JINAN
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