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Journal ArticleDOI

Digital camera identification from sensor pattern noise

01 Nov 2006-IEEE Transactions on Information Forensics and Security (IEEE SIGNAL PROCESSING SOCIETY)-Vol. 1, Iss: 2, pp 205-214
TL;DR: A new method is proposed for the problem of digital camera identification from its images based on the sensor's pattern noise, which serves as a unique identification fingerprint for each camera under investigation by averaging the noise obtained from multiple images using a denoising filter.
Abstract: In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's pattern noise. For each camera under investigation, we first determine its reference pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using a denoising filter. To identify the camera from a given image, we consider the reference pattern noise as a spread-spectrum watermark, whose presence in the image is established by using a correlation detector. Experiments on approximately 320 images taken with nine consumer digital cameras are used to estimate false alarm rates and false rejection rates. Additionally, we study how the error rates change with common image processing, such as JPEG compression or gamma correction.

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Citations
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Journal ArticleDOI
TL;DR: A unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors is provided.
Abstract: In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.

850 citations


Cites background from "Digital camera identification from ..."

  • ...…factor common to all blocks (this factor may be due to processing uniformly applied to the whole image, such as lossy compression or filtering), we arrive at the following hypothesis testing problem: (10) where now , , is WGN with zero mean and known variance and , is a known constant ....

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  • ...The predictor is constructed as a mapping from some feature space to a real number in the interval [0,1]—the predicted value of ....

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  • ...Allowing these estimates to be accurate up to an unknown multiplicative factor common to all blocks (this factor may be due to processing uniformly applied to the whole image, such as lossy compression or filtering), we arrive at the following hypothesis testing problem: (10) where now , , is WGN…...

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Book ChapterDOI
21 Jul 2010
TL;DR: The degree to which modern web browsers are subject to "device fingerprinting" via the version and configuration information that they will transmit to websites upon request is investigated, and what countermeasures may be appropriate to prevent it is discussed.
Abstract: We investigate the degree to which modern web browsers are subject to "device fingerprinting" via the version and configuration information that they will transmit to websites upon request. We implemented one possible fingerprinting algorithm, and collected these fingerprints from a large sample of browsers that visited our test side, panopticlick.eff.org. We observe that the distribution of our fingerprint contains at least 18.1 bits of entropy, meaning that if we pick a browser at random, at best we expect that only one in 286,777 other browsers will share its fingerprint. Among browsers that support Flash or Java, the situation is worse, with the average browser carrying at least 18.8 bits of identifying information. 94.2% of browsers with Flash or Java were unique in our sample. By observing returning visitors, we estimate how rapidly browser fingerprints might change over time. In our sample, fingerprints changed quite rapidly, but even a simple heuristic was usually able to guess when a fingerprint was an "upgraded" version of a previously observed browser's fingerprint, with 99.1% of guesses correct and a false positive rate of only 0.86%. We discuss what privacy threat browser fingerprinting poses in practice, and what countermeasures may be appropriate to prevent it. There is a tradeoff between protection against fingerprintability and certain kinds of debuggability, which in current browsers is weighted heavily against privacy. Paradoxically, anti-fingerprinting privacy technologies can be self-defeating if they are not used by a sufficient number of people; we show that some privacy measures currently fall victim to this paradox, but others do not.

846 citations


Cites background from "Digital camera identification from ..."

  • ...Cameras [1,2], typewriters [3], and quartz crystal clocks [4,5] are among the devices that can be ? Thanks to my colleagues at EFF for their help with many aspects of this project, especially Seth Schoen, Tim Jones, Hugh D’Andrade, Chris Controllini, Stu Matthews, Rebecca Jeschke and Cindy Cohn; to Jered Wierzbicki, John Buckman and Igor Serebryany for MySQL advice; and to Andrew Clausen, Arvind Narayanan and Jonathan Mayer for helpful discussions about the data....

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Journal ArticleDOI
Hany Farid1
TL;DR: The field of digital forensics has emerged to help restore some trust to digital images and the author reviews the state of the art in this new and exciting field.
Abstract: We are undoubtedly living in an age where we are exposed to a remarkable array of visual imagery. While we may have historically had confidence in the integrity of this imagery, today's digital technology has begun to erode this trust. From the tabloid magazines to the fashion industry and in mainstream media outlets, scientific journals, political campaigns, courtrooms, and the photo hoaxes that land in our e-mail in-boxes, doctored photographs are appearing with a growing frequency and sophistication. Over the past five years, the field of digital forensics has emerged to help restore some trust to digital images. The author reviews the state of the art in this new and exciting field.

825 citations

Proceedings ArticleDOI
22 Mar 2010
TL;DR: A novel image database specifically built for the purpose of development and bench-marking of camera-based digital forensic techniques and is intended to become a useful resource for researchers and forensic investigators.
Abstract: This paper introduces and documents a novel image database specifically built for the purpose of development and bench-marking of camera-based digital forensic techniques. More than 14,000 images of various indoor and outdoor scenes have been acquired under controlled and thus widely comparable conditions from altogether 73 digital cameras. The cameras were drawn from only 25 different models to ensure that device-specific and model-specific characteristics can be disentangled and studied separately, as validated with results in this paper. In addition, auxiliary images for the estimation of device-specific sensor noise pattern were collected for each camera. Another subset of images to study model-specific JPEG compression algorithms has been compiled for each model. The 'Dresden Image Database' will be made freely available for scientific purposes when this accompanying paper is presented. The database is intended to become a useful resource for researchers and forensic investigators. Using a standard database as a benchmark not only makes results more comparable and reproducible, but it is also more economical and avoids potential copyright and privacy issues that go along with self-sampled benchmark sets from public photo communities on the Internet.

448 citations

Proceedings ArticleDOI
18 Mar 2015
TL;DR: How RAISE has been collected and organized is described, how digital image forensics and many other multimedia research areas may benefit of this new publicly available benchmark dataset and a very recent forensic technique for JPEG compression detection is tested.
Abstract: Digital forensics is a relatively new research area which aims at authenticating digital media by detecting possible digital forgeries. Indeed, the ever increasing availability of multimedia data on the web, coupled with the great advances reached by computer graphical tools, makes the modification of an image and the creation of visually compelling forgeries an easy task for any user. This in turns creates the need of reliable tools to validate the trustworthiness of the represented information. In such a context, we present here RAISE, a large dataset of 8156 high-resolution raw images, depicting various subjects and scenarios, properly annotated and available together with accompanying metadata. Such a wide collection of untouched and diverse data is intended to become a powerful resource for, but not limited to, forensic researchers by providing a common benchmark for a fair comparison, testing and evaluation of existing and next generation forensic algorithms. In this paper we describe how RAISE has been collected and organized, discuss how digital image forensics and many other multimedia research areas may benefit of this new publicly available benchmark dataset and test a very recent forensic technique for JPEG compression detection.

440 citations

References
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Book
24 Oct 2001
TL;DR: Digital Watermarking covers the crucial research findings in the field and explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied.
Abstract: Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

2,849 citations


"Digital camera identification from ..." refers methods in this paper

  • ...Because of the noise-like character of the PNU noise, it is natural to detect its presence in an image using correlation as is commonly done in robust watermark detection [11]....

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Book
01 Jan 2001
TL;DR: In this article, the spectral response, charge collection, charge transfer, and readout noise properties of charge-coupled devices have been investigated, and their potential for future improvement is discussed.
Abstract: The charge-coupled device dominates an ever-increasing variety of scientific imaging and spectroscopy applications. Recent experience indicates, however, that the full potential of CCD performance lies well beyond that realized in devices currently available.Test data suggest that major improvements are feasible in spectral response, charge collection, charge transfer, and readout noise. These properties, their measurement in existing CCDs, and their potential for future improvement are discussed in this paper.

1,297 citations


"Digital camera identification from ..." refers background in this paper

  • ...While it is commonly done for astronomical imaging,2consumer digital cameras do not flat field their images because it is difficult to achieve uniform sensor illumination inside the camera....

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  • ...The heart of every digital camera is the imaging sensor....

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  • ...This is partly because of the shot noise (also known as photonic noise [7], [8]), which is a random component, and partly because of the pattern noise—a deterministic component that stays approximately the same if multiple pictures of the exact same scene are taken....

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Book ChapterDOI
Siwei Lyu1, Hany Farid1
07 Oct 2002
TL;DR: In this article, a wavelet-like decomposition is used to build higher-order statistical models of natural images and support vector machines are then used to discriminate between untouched and adulterated images.
Abstract: Techniques for information hiding have become increasingly more sophisticated and widespread. With high-resolution digital images as carriers, detecting hidden messages has become considerably more difficult. This paper describes an approach to detecting hidden messages in images that uses a wavelet-like decomposition to build higher-order statistical models of natural images. Support vector machines are then used to discriminate between untouched and adulterated images.

529 citations

Book ChapterDOI
23 May 2004
TL;DR: This work describes several statistical techniques for detecting traces of digital tampering in the absence of any digital watermark or signature, and quantifies statistical correlations that result from specific forms ofdigital tampering.
Abstract: A digitally altered photograph, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic photograph. As a result, photographs no longer hold the unique stature as a definitive recording of events. We describe several statistical techniques for detecting traces of digital tampering in the absence of any digital watermark or signature. In particular, we quantify statistical correlations that result from specific forms of digital tampering, and devise detection schemes to reveal these correlations.

467 citations


"Digital camera identification from ..." refers methods in this paper

  • ...Techniques, such as the one described in [ 16 ], may help us alleviate the computational complexity of brute force searches by retrieving some information about applied geometrical operations....

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Proceedings Article
01 Jan 1998
TL;DR: This paper proposes statistical tests for the existence of sheep, goats, lambs and wolves and applies these tests to hunt for such animals using results from the 1998 NIST speaker recognition evaluation.
Abstract: : Performance variability in speech and speaker recognition systems can be attributed to many factors. One major factor, which is often acknowledged but seldom analyzed, is inherent differences in the recognizability of different speakers. In speaker recognition systems such differences are characterized by the use of animal names for different types of speakers, including sheep, goats, lambs and wolves, depending on their behavior with respect to automatic recognition systems. In this paper we propose statistical tests for the existence of these animals and apply these tests to hunt for such animals using results from the 1998 NIST speaker recognition evaluation.

444 citations


"Digital camera identification from ..." refers methods in this paper

  • ...Digital Object Identifier 10.1109/TIFS.2006.873602 taken (exposure, date, and time, etc.)....

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  • ...However, images are not typically stored in raw formats and are only available as TIFF/JPEG, which means they are already processed in the camera [2, Sec....

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  • ...All images were converted to 24-b TIFF using Sigma PhotoPro 2.1....

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  • ...Images taken in the Nikon NEF raw format were converted by Nikon Capture 4.0 into the 24-b true color TIFF format....

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  • ...Images in the Canon CRW raw format were converted using the Canon Utilities RAW Image Converter version 1.2.1 to the 24-b true-color TIFF format....

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