%0 Journal Article
%J Information Forensics and Security, IEEE Transactions on
%D 2009
%T Fingerprinting Compressed Multimedia Signals
%A Varna,A.L.
%A He,Shan
%A Swaminathan,A.
%A M. Wu
%K coding;
%K coding;copy
%K collusion;digital
%K communication;video
%K compression;digital
%K compression;video
%K dither;collusion
%K domain
%K fingerprinting;anti-collusion
%K fingerprinting;multimedia
%K Gaussian
%K management;multimedia
%K protection;data
%K resistance;compressed
%K rights
%X Digital fingerprinting is a technique to deter unauthorized redistribution of multimedia content by embedding a unique identifying signal in each legally distributed copy. The embedded fingerprint can later be extracted and used to trace the originator of an unauthorized copy. A group of users may collude and attempt to create a version of the content that cannot be traced back to any of them. As multimedia data is commonly stored in compressed form, this paper addresses the problem of fingerprinting compressed signals. Analysis is carried out to show that due to the quantized nature of the host signal and the embedded fingerprint, directly extending traditional fingerprinting techniques for uncompressed signals to the compressed case leads to low collusion resistance. To overcome this problem and improve the collusion resistance, a new technique for fingerprinting compressed signals called Anti-Collusion Dither (ACD) is proposed, whereby a random dither signal is added to the compressed host before embedding so as to make the effective host signal appear more continuous. The proposed technique is shown to reduce the accuracy with which attackers can estimate the host signal, and from an information theoretic perspective, the proposed ACD technique increases the maximum number of users that can be supported by the fingerprinting system under a given attack. Both analytical and experimental studies confirm that the proposed technique increases the probability of identifying a guilty user and can approximately quadruple the collusion resistance compared to conventional Gaussian fingerprinting.
%B Information Forensics and Security, IEEE Transactions on
%V 4
%P 330 - 345
%8 2009/09//
%@ 1556-6013
%G eng
%N 3
%R 10.1109/TIFS.2009.2025860
%0 Conference Paper
%B Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
%D 2008
%T Context and observation driven latent variable model for human pose estimation
%A Gupta,A.
%A Chen,T.
%A Chen,F.
%A Kimber,D.
%A Davis, Larry S.
%K estimation;
%K estimation;image
%K Gaussian
%K gestures;pose
%K latent
%K learning;parameterized
%K model;human
%K observations;integrated
%K pose
%K process
%K processes;gesture
%K processing;pose
%K recognition;image
%K tracking;Gaussian
%K variable
%X Current approaches to pose estimation and tracking can be classified into two categories: generative and discriminative. While generative approaches can accurately determine human pose from image observations, they are computationally expensive due to search in the high dimensional human pose space. On the other hand, discriminative approaches do not generalize well, but are computationally efficient. We present a hybrid model that combines the strengths of the two in an integrated learning and inference framework. We extend the Gaussian process latent variable model (GPLVM) to include an embedding from observation space (the space of image features) to the latent space. GPLVM is a generative model, but the inclusion of this mapping provides a discriminative component, making the model observation driven. Observation Driven GPLVM (OD-GPLVM) not only provides a faster inference approach, but also more accurate estimates (compared to GPLVM) in cases where dynamics are not sufficient for the initialization of search in the latent space. We also extend OD-GPLVM to learn and estimate poses from parameterized actions/gestures. Parameterized gestures are actions which exhibit large systematic variation in joint angle space for different instances due to difference in contextual variables. For example, the joint angles in a forehand tennis shot are function of the height of the ball (Figure 2). We learn these systematic variations as a function of the contextual variables. We then present an approach to use information from scene/objects to provide context for human pose estimation for such parameterized actions.
%B Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
%P 1 - 8
%8 2008/06//
%G eng
%R 10.1109/CVPR.2008.4587511
%0 Conference Paper
%B Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
%D 2008
%T Kernel integral images: A framework for fast non-uniform filtering
%A Hussein,M.
%A Porikli, F.
%A Davis, Larry S.
%K approximated
%K equations;interpolation;
%K filtering;bilinear
%K filtering;kernel
%K Gaussian
%K graphics;computer
%K images;approximation
%K integral
%K interpolation;computer
%K nonuniform
%K processing;integral
%K theory;filtering
%K theory;image
%K vision;fast
%K weighting
%X Integral images are commonly used in computer vision and computer graphics applications. Evaluation of box filters via integral images can be performed in constant time, regardless of the filter size. Although Heckbert (1986) extended the integral image approach for more complex filters, its usage has been very limited, in practice. In this paper, we present an extension to integral images that allows for application of a wide class of non-uniform filters. Our approach is superior to Heckbertpsilas in terms of precision requirements and suitability for parallelization. We explain the theoretical basis of the approach and instantiate two concrete examples: filtering with bilinear interpolation, and filtering with approximated Gaussian weighting. Our experiments show the significant speedups we achieve, and the higher accuracy of our approach compared to Heckbertpsilas.
%B Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
%P 1 - 8
%8 2008/06//
%G eng
%R 10.1109/CVPR.2008.4587641
%0 Conference Paper
%B Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
%D 2007
%T Colluding Fingerprinted Video using the Gradient Attack
%A He,Shan
%A Kirovski,D.
%A M. Wu
%K attack;multimedia
%K attacks;digital
%K content
%K data;video
%K distribution;fingerprint
%K effort;gradient
%K fingerprinted
%K fingerprinting;disproportional
%K fingerprints;colluding
%K fingerprints;Laplace
%K Gaussian
%K identification;multimedia
%K of
%K processing;
%K protection;unauthorized
%K signal
%K spectrum
%K spread
%K systems;security
%K video;collusion
%X Digital fingerprinting is an emerging tool to protect multimedia content from unauthorized distribution by embedding a unique fingerprint into each user's copy. Although several fingerprinting schemes have been proposed in related work, disproportional effort has been targeted towards identifying effective collusion attacks on fingerprinting schemes. Recent introduction of the gradient attack has refined the definition of an optimal attack and demonstrated strong effect on direct-sequence, uniformly distributed, and Gaussian spread spectrum fingerprints when applied to synthetic signals. In this paper, we apply the gradient attack on an existing well-engineered video fingerprinting scheme, refine the attack procedure, and demonstrate that the gradient attack is effective on Laplace fingerprints. Finally, we explore an improvement on fingerprint design to thwart the gradient attack. Results suggest that Laplace fingerprint should be avoided. However, we show that a signal mixed of Laplace and Gaussian fingerprints may serve as a design strategy to disable the gradient attack and force pirates into averaging as a form of adversary collusion.
%B Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
%V 2
%P II-161 -II-164 - II-161 -II-164
%8 2007/04//
%G eng
%R 10.1109/ICASSP.2007.366197
%0 Conference Paper
%B Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
%D 2007
%T Collusion-Resistant Fingerprinting for Compressed Multimedia Signals
%A Varna,A.L.
%A He,Shan
%A Swaminathan,A.
%A M. Wu
%A Lu,Haiming
%A Lu,Zengxiang
%K based
%K compression;Gaussian
%K compression;multimedia
%K dithering;collusion-resistant
%K fingerprinting;multimedia
%K Gaussian
%K processes;data
%K sequences;anticollusion
%K signals
%K spectrum
%K spread
%K systems;
%X Most existing collusion-resistant fingerprinting techniques are for fingerprinting uncompressed signals. In this paper, we first study the performance of the traditional Gaussian based spread spectrum sequences for fingerprinting compressed signals and show that the system can be easily defeated by averaging or taking the median of a few copies. To overcome the collusion problem for compressed multimedia host signals, we propose a technique called anti-collusion dithering to mimic an uncompressed signal. Results show higher probability of catching a colluder using the proposed scheme compared to using Gaussian based fingerprints.
%B Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
%V 2
%P II-165 -II-168 - II-165 -II-168
%8 2007/04//
%G eng
%R 10.1109/ICASSP.2007.366198
%0 Conference Paper
%B Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on
%D 2005
%T Efficient mean-shift tracking via a new similarity measure
%A Yang,Changjiang
%A Duraiswami, Ramani
%A Davis, Larry S.
%K algorithm;
%K analysis;
%K Bhattacharyya
%K coefficient;
%K Color
%K colour
%K density
%K divergence;
%K estimates;
%K extraction;
%K fast
%K feature
%K frame-rate
%K Gauss
%K Gaussian
%K histograms;
%K image
%K Kernel
%K Kullback-Leibler
%K matching;
%K Mean-shift
%K measures;
%K nonparametric
%K processes;
%K sample-based
%K sequences;
%K similarity
%K spaces;
%K spatial-feature
%K tracking
%K tracking;
%K transform;
%X The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the Kullback-Leibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the sample-based classical similarity measures require a calculation that is quadratic in the number of samples, making real-time performance difficult. To deal with these difficulties we propose a new, simple-to-compute and more discriminative similarity measure in spatial-feature spaces. The new similarity measure allows the mean shift algorithm to track more general motion models in an integrated way. To reduce the complexity of the computation to linear order we employ the recently proposed improved fast Gauss transform. This leads to a very efficient and robust nonparametric spatial-feature tracking algorithm. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.
%B Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on
%V 1
%P 176 - 183 vol. 1 - 176 - 183 vol. 1
%8 2005/06//
%G eng
%R 10.1109/CVPR.2005.139
%0 Conference Paper
%B Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on
%D 2005
%T Kernel-based Bayesian filtering for object tracking
%A Han,Bohyung
%A Zhu,Ying
%A Comaniciu, D.
%A Davis, Larry S.
%K approach;
%K approximation;
%K Bayes
%K Bayesian
%K Carlo
%K density
%K detection;
%K filtering;
%K functions;
%K Gaussian
%K interpolation;
%K kernel-based
%K methods;
%K mixtures;
%K Monte
%K nonGaussian
%K nonlinear
%K object
%K particle
%K probability
%K probability;
%K processes;
%K recognition;
%K sampling
%K sampling;
%K sequences;
%K system;
%K tracking;
%K video
%X Particle filtering provides a general framework for propagating probability density functions in nonlinear and non-Gaussian systems. However, the algorithm is based on a Monte Carlo approach and sampling is a problematic issue, especially for high dimensional problems. This paper presents a new kernel-based Bayesian filtering framework, which adopts an analytic approach to better approximate and propagate density functions. In this framework, the techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities by Gaussian mixtures, where all parameters such as the number of mixands, their weight, mean, and covariance are automatically determined. The proposed analytic approach is shown to perform sampling more efficiently in high dimensional space. We apply our algorithm to real-time tracking problems, and demonstrate its performance on real video sequences as well as synthetic examples.
%B Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on
%V 1
%P 227 - 234 vol. 1 - 227 - 234 vol. 1
%8 2005/06//
%G eng
%R 10.1109/CVPR.2005.199
%0 Conference Paper
%B Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on
%D 2005
%T On-line density-based appearance modeling for object tracking
%A Han,B.
%A Davis, Larry S.
%K appearance
%K approximation;Gaussian
%K Computer
%K density
%K density-based
%K detection;target
%K Gaussian
%K mixtures;object
%K modeling
%K modeling;real-time
%K processes;computer
%K sequences;object
%K technique;online
%K tracking;
%K tracking;online
%K vision;image
%K vision;sequential
%X Object tracking is a challenging problem in real-time computer vision due to variations of lighting condition, pose, scale, and view-point over time. However, it is exceptionally difficult to model appearance with respect to all of those variations in advance; instead, on-line update algorithms are employed to adapt to these changes. We present a new on-line appearance modeling technique which is based on sequential density approximation. This technique provides accurate and compact representations using Gaussian mixtures, in which the number of Gaussians is automatically determined. This procedure is performed in linear time at each time step, which we prove by amortized analysis. Features for each pixel and rectangular region are modeled together by the proposed sequential density approximation algorithm, and the target model is updated in scale robustly. We show the performance of our method by simulations and tracking in natural videos
%B Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on
%V 2
%P 1492 -1499 Vol. 2 - 1492 -1499 Vol. 2
%8 2005/10//
%G eng
%R 10.1109/ICCV.2005.181
%0 Conference Paper
%B Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
%D 2004
%T Iterative figure-ground discrimination
%A Zhao, L.
%A Davis, Larry S.
%K algorithm;
%K analysis;
%K Bandwidth
%K calculation;
%K Color
%K colour
%K Computer
%K density
%K dimensional
%K discrimination;
%K distribution;
%K distributions;
%K Estimation
%K estimation;
%K expectation
%K figure
%K Gaussian
%K ground
%K image
%K initialization;
%K iterative
%K Kernel
%K low
%K methods;
%K mixture;
%K model
%K model;
%K nonparametric
%K parameter
%K parametric
%K processes;
%K sampling
%K sampling;
%K segmentation
%K segmentation;
%K statistics;
%K theory;
%K vision;
%X Figure-ground discrimination is an important problem in computer vision. Previous work usually assumes that the color distribution of the figure can be described by a low dimensional parametric model such as a mixture of Gaussians. However, such approach has difficulty selecting the number of mixture components and is sensitive to the initialization of the model parameters. In this paper, we employ non-parametric kernel estimation for color distributions of both the figure and background. We derive an iterative sampling-expectation (SE) algorithm for estimating the color, distribution and segmentation. There are several advantages of kernel-density estimation. First, it enables automatic selection of weights of different cues based on the bandwidth calculation from the image itself. Second, it does not require model parameter initialization and estimation. The experimental results on images of cluttered scenes demonstrate the effectiveness of the proposed algorithm.
%B Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
%V 1
%P 67 - 70 Vol.1 - 67 - 70 Vol.1
%8 2004/08//
%G eng
%R 10.1109/ICPR.2004.1334006
%0 Journal Article
%J Signal Processing, IEEE Transactions on
%D 2003
%T Anti-collusion fingerprinting for multimedia
%A Trappe,W.
%A M. Wu
%A Wang,Z.J.
%A Liu,K. J.R
%K (mathematics);
%K additive
%K algorithm;
%K and
%K anti-collusion
%K attack;
%K averaging
%K binary
%K code
%K codes;
%K codevectors;
%K coding;
%K colluders
%K collusion;
%K combinatorial
%K communication;
%K compression;
%K correlation;
%K cost-effective
%K data
%K data;
%K design
%K DETECTION
%K detection;
%K digital
%K embedding;
%K fingerprinting;
%K Gaussian
%K identification;
%K image
%K images;
%K keying;
%K logical
%K mathematics;
%K Modulation
%K modulation;
%K multimedia
%K multimedia;
%K of
%K on-off
%K operation;
%K orthogonal
%K processes;
%K real
%K redistribution;
%K Security
%K signal
%K signals;
%K theory;
%K tree-structured
%K TREES
%K watermarking;
%X Digital fingerprinting is a technique for identifying users who use multimedia content for unintended purposes, such as redistribution. These fingerprints are typically embedded into the content using watermarking techniques that are designed to be robust to a variety of attacks. A cost-effective attack against such digital fingerprints is collusion, where several differently marked copies of the same content are combined to disrupt the underlying fingerprints. We investigate the problem of designing fingerprints that can withstand collusion and allow for the identification of colluders. We begin by introducing the collusion problem for additive embedding. We then study the effect that averaging collusion has on orthogonal modulation. We introduce a tree-structured detection algorithm for identifying the fingerprints associated with K colluders that requires O(Klog(n/K)) correlations for a group of n users. We next develop a fingerprinting scheme based on code modulation that does not require as many basis signals as orthogonal modulation. We propose a new class of codes, called anti-collusion codes (ACCs), which have the property that the composition of any subset of K or fewer codevectors is unique. Using this property, we can therefore identify groups of K or fewer colluders. We present a construction of binary-valued ACC under the logical AND operation that uses the theory of combinatorial designs and is suitable for both the on-off keying and antipodal form of binary code modulation. In order to accommodate n users, our code construction requires only O( radic;n) orthogonal signals for a given number of colluders. We introduce three different detection strategies that can be used with our ACC for identifying a suspect set of colluders. We demonstrate the performance of our ACC for fingerprinting multimedia and identifying colluders through experiments using Gaussian signals and real images.
%B Signal Processing, IEEE Transactions on
%V 51
%P 1069 - 1087
%8 2003/04//
%@ 1053-587X
%G eng
%N 4
%R 10.1109/TSP.2003.809378
%0 Conference Paper
%B Multimedia and Expo, 2003. ICME '03. Proceedings. 2003 International Conference on
%D 2003
%T Anti-collusion of group-oriented fingerprinting
%A Wang,Z.J.
%A M. Wu
%A Trappe,W.
%A Liu,K. J.R
%K anticollusion
%K attacks;
%K collusion
%K communication;
%K data;
%K digital
%K distributed
%K distribution;
%K fingerprinting;
%K fingerprints;
%K Gaussian
%K group-oriented
%K Internet;
%K method;
%K modulation;
%K multimedia
%K orthogonal
%K security;
%K Telecommunication
%K watermarking;
%X Digital fingerprinting of multimedia data involves embedding information in the content, and offers protection to the digital rights of the content by allowing illegitimate usage of the content to be identified by authorized parties. One potential threat to fingerprints is collusion, whereby a group of adversaries combine their individual copies in an attempt to remove the underlying fingerprints. Former studies indicate that collusion attacks based on a few dozen independent copies can confound a fingerprinting system that employs orthogonal modulation. However, since an adversary is more likely to collude with some users than other users, we propose a group-based fingerprinting scheme where users likely to collude with each other are assigned correlated fingerprints. We evaluate the performance of our group-based fingerprints by studying the collusion resistance of a fingerprinting system employing Gaussian distributed fingerprints. We compare the results to those of fingerprinting systems employing orthogonal modulation.
%B Multimedia and Expo, 2003. ICME '03. Proceedings. 2003 International Conference on
%V 2
%P II - 217-20 vol.2 - II - 217-20 vol.2
%8 2003/07//
%G eng
%R 10.1109/ICME.2003.1221592
%0 Conference Paper
%B Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
%D 2003
%T Nonlinear collusion attacks on independent fingerprints for multimedia
%A Zhao,Hong
%A M. Wu
%A Wang,Z.J.
%A Liu,K. J.R
%K attacks;
%K average
%K bounded
%K collusion
%K computing;
%K content
%K copies;
%K digital
%K distribution;
%K fingerprinted
%K fingerprinting;
%K fingerprints;
%K Gaussian
%K independent
%K multimedia
%K nonlinear
%K perceptual
%K quality;
%K robustness;
%K watermarking;
%X Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Collusion attack is a cost effective attack against digital fingerprinting where several copies with the same content but different fingerprints are combined to remove the original fingerprints. In this paper, we investigate average and nonlinear collusion attacks of independent Gaussian fingerprints and study both their effectiveness and the perceptual quality. We also propose the bounded Gaussian fingerprints to improve the perceptual quality of the fingerprinted copies. We further discuss the tradeoff between the robustness against collusion attacks and the perceptual quality of a fingerprinting system.
%B Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
%V 5
%P V - 664-7 vol.5 - V - 664-7 vol.5
%8 2003/04//
%G eng
%R 10.1109/ICASSP.2003.1200058
%0 Conference Paper
%B Multimedia and Expo, 2003. ICME '03. Proceedings. 2003 International Conference on
%D 2003
%T Performance of detection statistics under collusion attacks on independent multimedia fingerprints
%A Zhao,Hong
%A M. Wu
%A Wang,Z.J.
%A Liu,K. J.R
%K analysis;
%K attacks;
%K based
%K collusion
%K content;
%K DETECTION
%K digital
%K fingerprint
%K fingerprinting;
%K fingerprints;
%K Gaussian
%K identification;
%K independent
%K multimedia
%K performance;
%K preprocessing
%K processes;
%K redistribution;
%K security;
%K statistical
%K statistics;
%K systems;
%K techniques;
%K Telecommunication
%K unauthorized
%X Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Collusion attack is a cost effective attack against digital fingerprinting where several copies with the same content but different fingerprints are combined to remove the original fingerprints. In this paper, we consider average attack and several nonlinear collusion attacks on independent Gaussian based fingerprints, and study the detection performance of several commonly used detection statistics in the literature under collusion attacks. Observing that these detection statistics are not specifically designed for collusion scenarios and do not take into account the characteristics of the newly generated fingerprints under collusion attacks, we propose pre-processing techniques to improve the detection performance of the detection statistics under collusion attacks.
%B Multimedia and Expo, 2003. ICME '03. Proceedings. 2003 International Conference on
%V 1
%P I - 205-8 vol.1 - I - 205-8 vol.1
%8 2003/07//
%G eng
%R 10.1109/ICME.2003.1220890
%0 Conference Paper
%B Multimedia and Expo, 2003. ICME '03. Proceedings. 2003 International Conference on
%D 2003
%T Resistance of orthogonal Gaussian fingerprints to collusion attacks
%A Wang,Z.J.
%A M. Wu
%A Zhao,Hong
%A Liu,K. J.R
%A Trappe,W.
%K approach;
%K attacks;
%K capability;
%K collusion
%K data
%K data;
%K digital
%K distributed
%K embedded
%K fingerprinting;
%K fingerprints;
%K Gaussian
%K likelihood-based
%K modulation;
%K multimedia
%K of
%K orthogonal
%K probability;
%K processes;
%K protection;
%K Security
%K systems;
%K tracing
%X Digital fingerprinting is a means to offer protection to digital data by which fingerprints embedded in the multimedia are capable of identifying unauthorized use of digital content. A powerful attack that can be employed to reduce this tracing capability is collusion. In this paper, we study the collusion resistance of a fingerprinting system employing Gaussian distributed fingerprints and orthogonal modulation. We propose a likelihood-based approach to estimate the number of colluders, and introduce the thresholding detector for colluder identification. We first analyze the collusion resistance of a system to the average attack by considering the probability of a false negative and the probability of a false positive when identifying colluders. Lower and upper bounds for the maximum number of colluders K_{max} are derived. We then show that the detectors are robust to different attacks. We further study different sets of performance criteria.
%B Multimedia and Expo, 2003. ICME '03. Proceedings. 2003 International Conference on
%V 1
%P I - 617-20 vol.1 - I - 617-20 vol.1
%8 2003/07//
%G eng
%R 10.1109/ICME.2003.1220993