@conference {18285,
	title = {Tampering identification using Empirical Frequency Response},
	booktitle = {Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on},
	year = {2009},
	month = {2009/04//},
	pages = {1517 - 1520},
	abstract = {With the widespread popularity of digital images and the presence of easy-to-use image editing software, content integrity can no longer be taken for granted, and there is a strong need for techniques that not only detect the presence of tampering but also identify its type. This paper focusses on tampering-type identification and introduces a new approach based on the empirical frequency response (EFR) to address this problem. We show that several types of tampering operations, both linear shift invariant (LSI) and non-LSI, can be characterized consistently and distinctly by their EFRs. We then extend the approach to estimate the EFR for scenarios where only the final image is available. Theoretical reasoning supported by experimental results verify the effectiveness of this method for identifying the type of a tampering operation.},
	keywords = {aproach;JPEG, coding;multimedia, compression;frequency, compression;LSI, computing;security, data;, editing, EFR, forensics;tampering-type, Frequency, identification;data, image, invariant;multimedia, of, response;image, response;linear, shift, software;empirical, system;digital},
	doi = {10.1109/ICASSP.2009.4959884},
	author = {Chuang,Wei-Hong and Swaminathan,A. and M. Wu}
}
@conference {18224,
	title = {Image acquisition forensics: Forensic analysis to identify imaging source},
	booktitle = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/4},
	pages = {1657 - 1660},
	abstract = {With widespread availability of digital images and easy-to-use image editing softwares, the origin and integrity of digital images has become a serious concern. This paper introduces the problem of image acquisition forensics and proposes a fusion of a set of signal processing features to identify the source of digital images. Our results show that the devices{\textquoteright} color interpolation coefficients and noise statistics can jointly serve as good forensic features to help accurately trace the origin of the input image to its production process and to differentiate between images produced by cameras, cell phone cameras, scanners, and computer graphics. Further, the proposed features can also be extended to determining the brand and model of the device. Thus, the techniques introduced in this work provide a unified framework for image acquisition forensics.},
	keywords = {ACQUISITION, acquisition;image, analysis;, analysis;image, analysis;interpolation;statistical, cameras;color, cell, coefficients;computer, colour, editing, forensics;image, graphics;digital, identification;noise, images;forensic, Interpolation, phone, processing;data, softwares;imaging, source, statistics;scanners;signal},
	doi = {10.1109/ICASSP.2008.4517945},
	author = {McKay,C. and Swaminathan,A. and Gou,Hongmei and M. Wu}
}
@conference {18255,
	title = {Noise Features for Image Tampering Detection and Steganalysis},
	booktitle = {Image Processing, 2007. ICIP 2007. IEEE International Conference on},
	volume = {6},
	year = {2007},
	month = {2007/10/16/19},
	pages = {VI -97 -VI -100 - VI -97 -VI -100},
	abstract = {With increasing availability of low-cost image editing softwares, the authenticity of digital images can no longer be taken for granted. Digital images have also been used as cover data for transmitting secret information in the field of steganography. In this paper, we introduce a new set of features for multimedia forensics to determine if a digital image is an authentic camera output or if it has been tampered or embedded with hidden data. We perform such image forensic analysis employing three sets of statistical noise features, including those from denoising operations, wavelet analysis, and neighborhood prediction. Our experimental results demonstrate that the proposed method can effectively distinguish digital images from their tampered or stego versions.},
	keywords = {analysis;cryptography;data, analysis;image, analysis;wavelet, authenticity;hidden, computing;statistical, data;image, denoising, denoising;multimedia, detection;low-cost, editing, encapsulation;feature, extraction;image, features;steganalysis;wavelet, forensic, forensics;neighborhood, image, NOISE, operations;digital, prediction;statistical, softwares;multimedia, tampering, transforms;},
	doi = {10.1109/ICIP.2007.4379530},
	author = {Gou,Hongmei and Swaminathan,A. and M. Wu}
}
@conference {18226,
	title = {Image Tampering Identification using Blind Deconvolution},
	booktitle = {Image Processing, 2006 IEEE International Conference on},
	year = {2006},
	month = {2006/10//},
	pages = {2309 - 2312},
	abstract = {Digital images have been used in growing number of applications from law enforcement and surveillance, to medical diagnosis and consumer photography. With such widespread popularity and the presence of low-cost image editing softwares, the integrity of image content can no longer be taken for granted. In this paper, we propose a novel technique based on blind deconvolution to verify image authenticity. We consider the direct output images of a camera as authentic, and introduce algorithms to detect further processing such as tampering applied to the image. Our proposed method is based on the observation that many tampering operations can be approximated as a combination of linear and non-linear components. We model the linear part of the tampering process as a filter, and obtain its coefficients using blind deconvolution. These estimated coefficients are then used to identify possible manipulations. We demonstrate the effectiveness of the proposed image authentication technique and compare our results with existing works},
	keywords = {(access, approximation;blind, authentication;image, coding;, compression;deconvolution;filtering, control);data, deconvolution;camera;consumer, diagnosis;surveillance;tampering, editing, identification;approximation, images;filter, photography;digital, process;image, softwares;medical, theory;biometrics, theory;image},
	doi = {10.1109/ICIP.2006.312848},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
