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K.j. Ray Liu - One of the best experts on this subject based on the ideXlab platform.

  • Robust median filtering Forensics using an autoregressive model
    IEEE Transactions on Information Forensics and Security, 2013
    Co-Authors: Xiangui Kang, Anjie Peng, Matthew C. Stamm, K.j. Ray Liu
    Abstract:

    In order to verify the authenticity of digital images, researchers have begun developing digital Forensic Techniques to identify image editing. One editing operation that has recently received increased attention is median filtering. While several median filtering detection Techniques have recently been developed, their performance is degraded by JPEG compression. These Techniques suffer similar degradations in performance when a small window of the image is analyzed, as is done in localized filtering or cut-and-paste detection, rather than the image as a whole. In this paper, we propose a new, robust median filtering Forensic Technique. It operates by analyzing the statistical properties of the median filter residual (MFR), which we define as the difference between an image in question and a median filtered version of itself. To capture the statistical properties of the MFR, we fit it to an autoregressive (AR) model. We then use the AR coefficients as features for median filter detection. We test the effectiveness of our proposed median filter detection Techniques through a series of experiments. These results show that our proposed Forensic Technique can achieve important performance gains over existing methods, particularly at low false-positive rates, with a very small dimension of features.

  • Anti-Forensics of median filtering
    ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2013
    Co-Authors: Zhung Han Wu, Matthew C. Stamm, K.j. Ray Liu
    Abstract:

    A number of Forensic Techniques have been developed to identify the use of digital multimedia editing operations. In response, several anti-Forensic operations have been designed to fool Forensic algorithms. One operation that has received considerable attention is median filtering, since it can be used for image enhancement or anti-Forensic purposes. As a result, several median filtering detectors have been developed. In this paper, we propose an anti-Forensic Technique to disguise the use of median filtering. We do this by first proposing a model for an unaltered image's pixel difference distribution. We then modify a median filter image's pixel difference distribution using anti-Forensic noise so that it no longer contains median filtering fingerprints. Through a series of experiments, we are able to show that our anti-Forensic Technique can fool existing median filtering detectors under realistic conditions.

  • Forensic detection of image manipulation using statistical intrinsic fingerprints
    IEEE Transactions on Information Forensics and Security, 2010
    Co-Authors: Matthew C. Stamm, K.j. Ray Liu
    Abstract:

    As the use of digital images has increased, so has the means and the incentive to create digital image forgeries. Accordingly, there is a great need for digital image Forensic Techniques capable of detecting image alterations and forged images. A number of image processing operations, such as histogram equalization or gamma correction, are equivalent to pixel value mappings. In this paper, we show that pixel value mappings leave behind statistical traces, which we shall refer to as a mapping's intrinsic fingerprint, in an image's pixel value histogram. We then propose Forensic methods for detecting general forms globally and locally applied contrast enhancement as well as a method for identifying the use of histogram equalization by searching for the identifying features of each operation's intrinsic fingerprint. Additionally, we propose a method to detect the global addition of noise to a previously JPEG-compressed image by observing that the intrinsic fingerprint of a specific mapping will be altered if it is applied to an image's pixel values after the addition of noise. Through a number of simulations, we test the efficacy of each proposed Forensic Technique. Our simulation results show that aside from exceptional cases, all of our detection methods are able to correctly detect the use of their designated image processing operation with a probability of 99% given a false alarm probability of 7% or less.

  • Anti-Forensics of JPEG compression
    ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2010
    Co-Authors: Matthew C. Stamm, Steven K. Tjoa, W. Sabrina Lin, K.j. Ray Liu
    Abstract:

    The widespread availability of photo editing software has made it easy to create visually convincing digital image forgeries. To address this problem, there has been much recent work in the field of digital image Forensics. There has been little work, however, in the field of anti-Forensics, which seeks to develop a set of Techniques designed to fool current Forensic methodologies. In this work, we present a Technique for disguising an image's JPEG compression history. An image's JPEG compression history can be used to provide evidence of image manipulation, supply information about the camera used to generate an image, and identify forged regions within an image. We show how the proper addition of noise to an image's discrete cosine transform coefficients can sufficiently remove quantization artifacts which act as indicators of JPEG compression while introducing an acceptable level of distortion. Simulation results are provided to verify the efficacy of this anti-Forensic Technique.

Matthew C. Stamm - One of the best experts on this subject based on the ideXlab platform.

  • Robust median filtering Forensics using an autoregressive model
    IEEE Transactions on Information Forensics and Security, 2013
    Co-Authors: Xiangui Kang, Anjie Peng, Matthew C. Stamm, K.j. Ray Liu
    Abstract:

    In order to verify the authenticity of digital images, researchers have begun developing digital Forensic Techniques to identify image editing. One editing operation that has recently received increased attention is median filtering. While several median filtering detection Techniques have recently been developed, their performance is degraded by JPEG compression. These Techniques suffer similar degradations in performance when a small window of the image is analyzed, as is done in localized filtering or cut-and-paste detection, rather than the image as a whole. In this paper, we propose a new, robust median filtering Forensic Technique. It operates by analyzing the statistical properties of the median filter residual (MFR), which we define as the difference between an image in question and a median filtered version of itself. To capture the statistical properties of the MFR, we fit it to an autoregressive (AR) model. We then use the AR coefficients as features for median filter detection. We test the effectiveness of our proposed median filter detection Techniques through a series of experiments. These results show that our proposed Forensic Technique can achieve important performance gains over existing methods, particularly at low false-positive rates, with a very small dimension of features.

  • Anti-Forensics of median filtering
    ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2013
    Co-Authors: Zhung Han Wu, Matthew C. Stamm, K.j. Ray Liu
    Abstract:

    A number of Forensic Techniques have been developed to identify the use of digital multimedia editing operations. In response, several anti-Forensic operations have been designed to fool Forensic algorithms. One operation that has received considerable attention is median filtering, since it can be used for image enhancement or anti-Forensic purposes. As a result, several median filtering detectors have been developed. In this paper, we propose an anti-Forensic Technique to disguise the use of median filtering. We do this by first proposing a model for an unaltered image's pixel difference distribution. We then modify a median filter image's pixel difference distribution using anti-Forensic noise so that it no longer contains median filtering fingerprints. Through a series of experiments, we are able to show that our anti-Forensic Technique can fool existing median filtering detectors under realistic conditions.

  • Temporal Forensics and Anti-Forensics for Motion Compensated Video
    IEEE Transactions on Information Forensics and Security, 2012
    Co-Authors: Matthew C. Stamm, Sabrina W. Lin, K. Ray J. Liu
    Abstract:

    Due to the ease with which digital information can be altered, many digital Forensic Techniques have been developed to authenticate multimedia content. Similarly, a number of anti-Forensic operations have recently been designed to make digital forgeries undetectable by Forensic Techniques. However, like the digital manipulations they are designed to hide, many anti-Forensic operations leave behind their own Forensically detectable traces. As a result, a digital forger must balance the trade-off between completely erasing evidence of their forgery and introducing new evidence of anti-Forensic manipulation. Because a Forensic investigator is typically bound by a constraint on their probability of false alarm (P_fa), they must also balance a trade-off between the accuracy with which they detect forgeries and the accuracy with which they detect the use of anti-Forensics. In this paper, we analyze the interaction between a forger and a Forensic investigator by examining the problem of authenticating digital videos. Specifically, we study the problem of adding or deleting a sequence of frames from a digital video. We begin by developing a theoretical model of the Forensically detectable fingerprints that frame deletion or addition leaves behind, then use this model to improve upon the video frame deletion or addition detection Technique proposed by Wang and Farid. Next, we propose an anti-Forensic Technique designed to fool video Forensic Techniques and develop a method for detecting the use of anti-Forensics. We introduce a new set of Techniques for evaluating the performance of anti-Forensic operations and develop a game theoretic framework for analyzing the interplay between a Forensic investigator and a forger. We use these new Techniques to evaluate the performance of each of our proposed Forensic and anti-Forensic Techniques, and identify the optimal actions of both the forger and Forensic investigator.

  • Forensic detection of image manipulation using statistical intrinsic fingerprints
    IEEE Transactions on Information Forensics and Security, 2010
    Co-Authors: Matthew C. Stamm, K.j. Ray Liu
    Abstract:

    As the use of digital images has increased, so has the means and the incentive to create digital image forgeries. Accordingly, there is a great need for digital image Forensic Techniques capable of detecting image alterations and forged images. A number of image processing operations, such as histogram equalization or gamma correction, are equivalent to pixel value mappings. In this paper, we show that pixel value mappings leave behind statistical traces, which we shall refer to as a mapping's intrinsic fingerprint, in an image's pixel value histogram. We then propose Forensic methods for detecting general forms globally and locally applied contrast enhancement as well as a method for identifying the use of histogram equalization by searching for the identifying features of each operation's intrinsic fingerprint. Additionally, we propose a method to detect the global addition of noise to a previously JPEG-compressed image by observing that the intrinsic fingerprint of a specific mapping will be altered if it is applied to an image's pixel values after the addition of noise. Through a number of simulations, we test the efficacy of each proposed Forensic Technique. Our simulation results show that aside from exceptional cases, all of our detection methods are able to correctly detect the use of their designated image processing operation with a probability of 99% given a false alarm probability of 7% or less.

  • Anti-Forensics of JPEG compression
    ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2010
    Co-Authors: Matthew C. Stamm, Steven K. Tjoa, W. Sabrina Lin, K.j. Ray Liu
    Abstract:

    The widespread availability of photo editing software has made it easy to create visually convincing digital image forgeries. To address this problem, there has been much recent work in the field of digital image Forensics. There has been little work, however, in the field of anti-Forensics, which seeks to develop a set of Techniques designed to fool current Forensic methodologies. In this work, we present a Technique for disguising an image's JPEG compression history. An image's JPEG compression history can be used to provide evidence of image manipulation, supply information about the camera used to generate an image, and identify forged regions within an image. We show how the proper addition of noise to an image's discrete cosine transform coefficients can sufficiently remove quantization artifacts which act as indicators of JPEG compression while introducing an acceptable level of distortion. Simulation results are provided to verify the efficacy of this anti-Forensic Technique.

Mauro Barni - One of the best experts on this subject based on the ideXlab platform.

Tim Hindle - One of the best experts on this subject based on the ideXlab platform.

  • Microbial source tracking: a Forensic Technique for microbial source identification?
    Journal of Environmental Monitoring, 2007
    Co-Authors: C M Stapleton, David KAY, M D Wyer, A Gawler, Jonathan Crowther, Michael Walters, A.t Mcdonald, Tim Hindle
    Abstract:

    As the requirements of the Water Framework Directive ( WFD) and the US Clean Water Act ( USCWA) for the maintenance of microbiological water quality in 'protected areas' highlight, there is a growing recognition that integrated management of point and diffuse sources of microbial pollution is essential. New information on catchment microbial dynamics and, in particular, the sources of faecal indicator bacteria found in bathing and shellfish harvesting waters is a pre-requisite for the design of any 'programme of measures' at the drainage basin scale to secure and maintain compliance with existing and new health-based microbiological standards. This paper reports on a catchment-scale microbial source tracking ( MST) study in the Leven Estuary drainage basin, northwest England, an area for which quantitative faecal indicator source apportionment empirical data and land use information were also collected. Since previous MST studies have been based on laboratory trials using 'manufactured' samples or analyses of spot environmental samples without the contextual microbial flux data ( under high and low flow conditions) and source information, such background data are needed to evaluate the utility of MST in USCWA total maximum daily load ( TMDL) assessments or WFD 'Programmes of Measures'. Thus, the operational utility of MST remains in some doubt. The results of this investigation, using genotyping of Bacteroidetes using polymerase chain reaction ( PCR) and male-specific ribonucleic acid coliphage ( F + RNA coliphage) using hybridisation, suggest some discrimination is possible between livestock- and human-derived faecal indicator concentrations but, in inter-grade areas, the degree to which the tracer picture reflected the land use pattern and probable faecal indicator loading were less distinct. Interestingly, the MST data was more reliable on high flow samples when much of the faecal indicator flux from catchment systems occurs. Whilst a useful supplementary tool, the MST information did not provide quantitative source apportionment for the study catchment. Thus, it could not replace detailed empirical measurement of microbial flux at key catchment outlets to underpin faecal indicator source apportionment. Therefore, the MST Techniques reported herein currently may not meet the standards required to be a useful Forensic tool, although continued development of the methods and further catchment scale studies could increase confidence in such methods for future application.

Xiangui Kang - One of the best experts on this subject based on the ideXlab platform.

  • countering anti Forensics of median filtering
    International Conference on Acoustics Speech and Signal Processing, 2014
    Co-Authors: Hui Zeng, Xiangui Kang, Tengfei Qin, Li Liu
    Abstract:

    The statistical fingerprints left by median filtering can be a valuable clue for image Forensics. However, these fingerprints may be maliciously erased by a forger. Recently, a tricky anti-Forensic method has been proposed to remove median filtering traces by restoring images' pixel difference distribution. In this paper, we analyze the traces of this anti-Forensic Technique and propose a novel counter method. The experimental results show that our method could reveal this anti-Forensics effectively at low computation load. According to our best knowledge, it's the first work on countering anti-Forensics of median filtering.

  • Robust median filtering Forensics using an autoregressive model
    IEEE Transactions on Information Forensics and Security, 2013
    Co-Authors: Xiangui Kang, Anjie Peng, Matthew C. Stamm, K.j. Ray Liu
    Abstract:

    In order to verify the authenticity of digital images, researchers have begun developing digital Forensic Techniques to identify image editing. One editing operation that has recently received increased attention is median filtering. While several median filtering detection Techniques have recently been developed, their performance is degraded by JPEG compression. These Techniques suffer similar degradations in performance when a small window of the image is analyzed, as is done in localized filtering or cut-and-paste detection, rather than the image as a whole. In this paper, we propose a new, robust median filtering Forensic Technique. It operates by analyzing the statistical properties of the median filter residual (MFR), which we define as the difference between an image in question and a median filtered version of itself. To capture the statistical properties of the MFR, we fit it to an autoregressive (AR) model. We then use the AR coefficients as features for median filter detection. We test the effectiveness of our proposed median filter detection Techniques through a series of experiments. These results show that our proposed Forensic Technique can achieve important performance gains over existing methods, particularly at low false-positive rates, with a very small dimension of features.