Digital camera identification based on analysis of optical defects

Abstract

In this paper we deal with the problem of digital camera identification by photographs. Identifying camera is possible by analyzing camera's sensor artifacts that occur during the process of photo processing. The problem of digital camera identification has been popular for a long time. Recently many effective and robust algorithms for solving this problem have been proposed. However, almost all solutions are based on state-of-the-art algorithm, proposed by Lukás et al. in 2006. Core of this algorithm is to calculate the so-called sensor pattern noise based on denoising images with wavelet-based denoising filter. Such technique is very efficient, but very time consuming. In this paper we consider tracing cameras by analyzing defects of their optical systems, like vignetting and lens distortion. We show that analysis of vignetting defect allows for recognizing brand of the camera. Lens distortion can be used to distinguish images from different cameras. Experimental evaluation was carried out on 60 devices (compact cameras and smartphones) for a total number of 12 051 images, with support of the Dresden Image Database. Proposed methods do not require denoising images with wavelet-based denoising filter what has a significant influence for speed of image processing, compared with state-of-the-art algorithm.

Source Camera Identification Techniques: A Survey

“…Bernacki [5] reported a digital camera identification technique using a real-time image processing system based on the investigation of vignetting and distortion flaws. The technique eliminates the need for a wavelet-based denoising filter or the creation of camera fingerprints, both of which have a significant impact on the image processing speed.…”

“…Only the suspicious digital image is available to forensic analysts, who can extract meaningful digital information from it to gather forensic evidence, track down the capture device, or discover any alteration therein. According to Bernacki [5], the internal traces or unique artifacts left by the digital camera in each digital image serve as camera fingerprints that are used in passive techniques, and investigating the image acquisition pipeline can offer these internal traces. In this paper a through critical literature survey on existing image camera source identification methods, their assessment criteria, and the publicly available dataset used to assess their performance are presented.…”