Digital image forensics via intrinsic fingerprints

Abstract

Abstract-Digital imaging has experienced tremendous growth in recent decades, and digital camera images have been used in a growing number of applications. With such increasing popularity and the availability of low-cost image editing software, the integrity of digital image content can no longer be taken for granted. This paper introduces a new methodology for the forensic analysis of digital camera images. The proposed method is based on the observation that many processing operations, both inside and outside acquisition devices, leave distinct intrinsic traces on digital images, and these intrinsic fingerprints can be identified and employed to verify the integrity of digital data. The intrinsic fingerprints of the various in-camera processing operations can be estimated through a detailed imaging model and its component analysis. Further processing applied to the camera captured image is modelled as a manipulation filter, for which a blind deconvolution technique is applied to obtain a linear time-invariant approximation and to estimate the intrinsic fingerprints associated with these postcamera operations. The absence of camera-imposed fingerprints from a test image indicates that the test image is not a camera output and is possibly generated by other image production processes. Any change or inconsistencies among the estimated camera-imposed fingerprints, or the presence of new types of fingerprints suggest that the image has undergone some kind of processing after the initial capture, such as tampering or steganographic embedding. Through analysis and extensive experimental studies, this paper demonstrates the effectiveness of the proposed framework for nonintrusive digital image forensics.

Fraud and Tamper Detection in Authenticity Verification through Gradient Based Image Reconstruction Technique for Security Systems

“…In fact, different from digital watermarking, forensics means are defined as "passive" because they can formulate an assessment on a digital document by resorting only to the digital asset itself. These techniques basically allow the user to determine if particular content has been tampered with [6], [7] or which was the acquisition device used [8], [9]. In particular, by focusing on the task of acquisition device identification, two main aspects must be studied: the first is to understand which kind of device has generated a digital image (e.g.…”

“…In particular, by focusing on the task of acquisition device identification, two main aspects must be studied: the first is to understand which kind of device has generated a digital image (e.g. a scanner, a digital camera or is a computer graphics product) [10], [11], while the second is to determine which specific camera or scanner (by recognizing model and brand) acquired that specific content [8], [9].…”

Fast image clustering of unknown source images

“…The main idea behind this kind of researches is WIFS'2010, December 12-15, 2010 that each sensor leaves a sort of unique fingerprint on the digital content it acquires due to some intrinsic imperfections and/or due to the specific acquisition process. Various solutions have been proposed in literature among these the use of CFA (Color Filter Array) characteristics [10] is quite well-known, nevertheless two seem to be the main followed approaches. The first one is based on the extraction, from images belonging to different categories (e.g scanned images, photos, etc.…”

Source class identification for DSLR and compact cameras

“…So, multimedia forensics try to find some answers to image integrity and authenticity to guarantee the credibility of digital images. Such solutions would provide useful forensic information to law enforcement and intelligence agencies about which kind of camera is used to acquire an image [1], [2], [3], [4] or whether it is doctored or not.…”

“…According to our best knowledge, this is the first work to identify DSLR and compact camera classes based on feature based classifiers. Available source camera identification methods utilizing PRNU noise [2], [3], color filter array (CFA) and demosaicing artifacts [4], [9] cannot be used in this problem.…”