Building a Robust and Informative System for SPN-based Image Forgery Detection

Beneficiary:  Xufeng Lin, University Of Warwick, xufeng.lin@warwick.ac.uk

Host:  Prof.  Andreas Uhl, University of Salzburg, uhl@cosy.sbg.ac.at

Period: 01/05/2015 to 31/05/2015

Place: Salzburg (Austria)

1.      Purpose of the STSM

 

The purpose of this STSM is to build a robust and informative system of SPN-based image forgery detection and enhance the collaboration between digital forensics and biometrics.

 

Although there are many approaches proposed to improve the performance of SPN-based image forgery detection, each of them only focuses on one or several components of the detection framework. An integrated approach for assembling the existing methods to provide superior performance is still lacking.  Therefore, we try to build a robust and informative system of SPN-based image forgery detection by combining the existing state-of-the-art methods. It would be also very interesting and reasonable to apply our experience of camera sensor identification to the analysis of biometric datasets in WaveLab.

2.      Description of the work carried out during the STSM

 

During the STSM, the following work has been carried out:

 

      • I first implemented several state-of-the-art SPN extraction, preprocessing and enhancement algorithms. Different combinations of these algorithms have been tried and evaluated aiming at achieving the “best” quality of camera fingerprint, which lies the foundation for SPN-based image forgery detection. The combination of the algorithms makes the SPN estimation more robust against the interferences introduced from the difference procedures in the image acquisition pipeline.

 

      • After obtaining the fingerprint of camera, image forgery is exposed by comparing the normalized cross correlation (NCC) between two patches from the same location of camera fingerprint and the noise residual of the suspicious image with a pre-defined threshold. As the computational cost of NCC, straightforwardly localizing the pixel-level forgery becomes computationally prohibitive. Therefore, a fast pixel-level NCC calculation method was proposed by pre-calculating the local mean and variance of the camera fingerprint and the noise residual of the suspicious image.

 

      • One problem of existing SPN-based image forgery detection algorithms is that the output is a binary image indicating the pixels have been forged, which will easily lead to omissions and false positives. By incorporating the prior knowledge about the quality of SPN as well as the theoretical analysis of the distribution of correlation values, I converted the detection results into a probability map, which can provide the forensic investigators with more rich and instructive information for decision-making.

 

      • Collaborated with Wavelab group, we frequently discussed and shared experience in the area of image clustering based on SPN. In the later phase of this STSM, we jointly conducted image clustering experiments on CASIA iris dataset and achieved very promising results.

 

      • Collaborated with Wavelab group, we devised a fast correlation-based image alignment algorithm using the noise residuals extracted from iris images. This alignment algorithm serves as a preprocessing step for sensor aging rate estimation.

 

 

 

3.      Future collaboration with the host institution (if applicable)

 

During the STSM, I worked with the members of Wavelab group and tried to apply the sensor pattern noise technology to analyze the biometric datasets. The outcome of the collaborative work shows the effectiveness of sensor pattern noise in analyzing the Iris databases. So the future collaboration will focus on extending the applications of sensor pattern noise in the area of digital biometrics.

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