@article {18230,
	title = {Intrinsic Sensor Noise Features for Forensic Analysis on Scanners and Scanned Images},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {4},
	year = {2009},
	month = {2009/09//},
	pages = {476 - 491},
	abstract = {A large portion of digital images available today are acquired using digital cameras or scanners. While cameras provide digital reproduction of natural scenes, scanners are often used to capture hard-copy art in a more controlled environment. In this paper, new techniques for nonintrusive scanner forensics that utilize intrinsic sensor noise features are proposed to verify the source and integrity of digital scanned images. Scanning noise is analyzed from several aspects using only scanned image samples, including through image denoising, wavelet analysis, and neighborhood prediction, and then obtain statistical features from each characterization. Based on the proposed statistical features of scanning noise, a robust scanner identifier is constructed to determine the model/brand of the scanner used to capture a scanned image. Utilizing these noise features, we extend the scope of acquisition forensics to differentiating scanned images from camera-taken photographs and computer-generated graphics. The proposed noise features also enable tampering forensics to detect postprocessing operations on scanned images. Experimental results are presented to demonstrate the effectiveness of employing the proposed noise features for performing various forensic analysis on scanners and scanned images.},
	keywords = {analysis;image, camera-taken, cameras;digital, denoising;intrinsic, denoising;wavelet, features;natural, forensics;scanned, graphics;digital, identifier;wavelet, images;digital, images;scanner, NOISE, photographs;computer-generated, prediction;nonintrusive, reproduction;forensic, scanner, scenes;neighborhood, sensor, transforms;},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2009.2026458},
	author = {Gou,Hongmei and Swaminathan,A. and M. Wu}
}
@article {18197,
	title = {Digital image forensics via intrinsic fingerprints},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {3},
	year = {2008},
	month = {2008/03//},
	pages = {101 - 117},
	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.},
	keywords = {ACQUISITION, analysis;intrinsic, approximation;cameras;digital, camera, deconvolution;digital, devices;blind, fingerprints;time, forensics;forensic, identification;image, image, images;digital, invariant, photography;fingerprint, sensors;},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2007.916010},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
