Anti-Forensics of Camera Identification and the Triangle Test by Improved Fingerprint-Copy Attack
This addresses anti-forensics for camera identification, specifically improving attacks to evade detection, but it is incremental as it builds on existing fingerprint-copy methods.
The paper tackles the problem of detecting fingerprint-copy attacks in camera identification by proposing an improved scheme that disperses the fingerprint using a block-wise method and random partial use of stolen images, reducing the impact of non-PRNU components; experiments on 2,900 images from 4 cameras show it effectively fools identification and degrades triangle test performance.
The fingerprint-copy attack aims to confuse camera identification based on sensor pattern noise. However, the triangle test shows that the forged images undergone fingerprint-copy attack would share a non-PRNU (Photo-response nonuniformity) component with every stolen image, and thus can detect fingerprint-copy attack. In this paper, we propose an improved fingerprint-copy attack scheme. Our main idea is to superimpose the estimated fingerprint into the target image dispersedly, via employing a block-wise method and using the stolen images randomly and partly. We also develop a practical method to determine the strength of the superimposed fingerprint based on objective image quality. In such a way, the impact of non-PRNU component on the triangle test is reduced, and our improved fingerprint-copy attack is difficultly detected. The experiments evaluated on 2,900 images from 4 cameras show that our scheme can effectively fool camera identification, and significantly degrade the performance of the triangle test simultaneously.