%0 Journal Article
%J Information Forensics and Security, IEEE Transactions on
%D 2008
%T Digital image forensics via intrinsic fingerprints
%A Swaminathan,A.
%A M. Wu
%A Liu,K. J.R
%K ACQUISITION
%K analysis;intrinsic
%K approximation;cameras;digital
%K camera
%K deconvolution;digital
%K devices;blind
%K fingerprints;time
%K forensics;forensic
%K identification;image
%K image
%K images;digital
%K invariant
%K photography;fingerprint
%K sensors;
%X 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.
%B Information Forensics and Security, IEEE Transactions on
%V 3
%P 101 - 117
%8 2008/03//
%@ 1556-6013
%G eng
%N 1
%R 10.1109/TIFS.2007.916010
%0 Conference Paper
%B Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
%D 2008
%T Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision
%A Turaga,P.
%A Veeraraghavan,A.
%A Chellapa, Rama
%K algorithm;learning
%K analysis;computer
%K analysis;statistical
%K analysis;video
%K based
%K classification;image
%K classification;spatio-temporal
%K distribution
%K distributions;
%K Face
%K functions;shape
%K Grassmann
%K invariant
%K manifold;activity
%K manifold;Stiefel
%K matching;inference
%K matching;spatiotemporal
%K measures;estimation
%K modeling;statistical
%K parameters;pattern
%K phenomena;statistical
%K recognition;affine
%K recognition;computer
%K recognition;probability
%K SHAPE
%K structure;image
%K technique;geometric
%K theory;manifold-valued
%K vision;distance
%K vision;image
%X Many applications in computer vision and pattern recognition involve drawing inferences on certain manifold-valued parameters. In order to develop accurate inference algorithms on these manifolds we need to a) understand the geometric structure of these manifolds b) derive appropriate distance measures and c) develop probability distribution functions (pdf) and estimation techniques that are consistent with the geometric structure of these manifolds. In this paper, we consider two related manifolds - the Stiefel manifold and the Grassmann manifold, which arise naturally in several vision applications such as spatio-temporal modeling, affine invariant shape analysis, image matching and learning theory. We show how accurate statistical characterization that reflects the geometry of these manifolds allows us to design efficient algorithms that compare favorably to the state of the art in these very different applications. In particular, we describe appropriate distance measures and parametric and non-parametric density estimators on these manifolds. These methods are then used to learn class conditional densities for applications such as activity recognition, video based face recognition and shape classification.
%B Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
%P 1 - 8
%8 2008/06//
%G eng
%R 10.1109/CVPR.2008.4587733
%0 Conference Paper
%B Content-Based Multimedia Indexing, 2007. CBMI '07. International Workshop on
%D 2007
%T Classifying Computer Generated Charts
%A Prasad,V. S.N
%A Siddiquie,B.
%A Golbeck,J.
%A Davis, Larry S.
%K algorithm;scale
%K analysis;visual
%K classification;image
%K database;image
%K databases;
%K feature
%K Internet;bar-chart;curve-plot;image
%K invariant
%K match
%K matching;image
%K relationship;surface-plot;Internet;image
%K representation;image
%K segmentation;pie-chart;pyramid
%K segmentation;statistical
%K transform;scatter-plot;spatial
%X We present an approach for classifying images of charts based on the shape and spatial relationships of their primitives. Five categories are considered: bar-charts, curve-plots, pie-charts, scatter-plots and surface-plots. We introduce two novel features to represent the structural information based on (a) region segmentation and (b) curve saliency. The local shape is characterized using the Histograms of Oriented Gradients (HOG) and the Scale Invariant Feature Transform (SIFT) descriptors. Each image is represented by sets of feature vectors of each modality. The similarity between two images is measured by the overlap in the distribution of the features -measured using the Pyramid Match algorithm. A test image is classified based on its similarity with training images from the categories. The approach is tested with a database of images collected from the Internet.
%B Content-Based Multimedia Indexing, 2007. CBMI '07. International Workshop on
%P 85 - 92
%8 2007/06//
%G eng
%R 10.1109/CBMI.2007.385396
%0 Conference Paper
%B Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on
%D 2007
%T Efficient Indexing For Articulation Invariant Shape Matching And Retrieval
%A Biswas,S.
%A Aggarwal,G.
%A Chellapa, Rama
%K alignment;image
%K articulation
%K geometric
%K invariant
%K matching;image
%K matching;indexing;invariant
%K relationships;shape-wise
%K retrieval;indexing;
%K retrieval;pairwise
%K SHAPE
%X Most shape matching methods are either fast but too simplistic to give the desired performance or promising as far as performance is concerned but computationally demanding. In this paper, we present a very simple and efficient approach that not only performs almost as good as many state-of-the-art techniques but also scales up to large databases. In the proposed approach, each shape is indexed based on a variety of simple and easily computable features which are invariant to articulations and rigid transformations. The features characterize pairwise geometric relationships between interest points on the shape, thereby providing robustness to the approach. Shapes are retrieved using an efficient scheme which does not involve costly operations like shape-wise alignment or establishing correspondences. Even for a moderate size database of 1000 shapes, the retrieval process is several times faster than most techniques with similar performance. Extensive experimental results are presented to illustrate the advantages of our approach as compared to the best in the field.
%B Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on
%P 1 - 8
%8 2007/06//
%G eng
%R 10.1109/CVPR.2007.383227
%0 Conference Paper
%B Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on
%D 2007
%T Robust Object Tracking with Regional Affine Invariant Features
%A Tran,Son
%A Davis, Larry S.
%K affine
%K algorithm;feature
%K analysis;image
%K analysis;pixel
%K consistency;regional
%K detection;motion
%K detection;tracking;
%K extraction;image
%K feature
%K features;robust
%K invariant
%K matching;image
%K MOTION
%K object
%K resolution;object
%K tracking
%X We present a tracking algorithm based on motion analysis of regional affine invariant image features. The tracked object is represented with a probabilistic occupancy map. Using this map as support, regional features are detected and probabilistically matched across frames. The motion of pixels is then established based on the feature motion. The object occupancy map is in turn updated according to the pixel motion consistency. We describe experiments to measure the sensitivities of our approach to inaccuracy in initialization, and compare it with other approaches.
%B Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on
%P 1 - 8
%8 2007/10//
%G eng
%R 10.1109/ICCV.2007.4408948
%0 Conference Paper
%B Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on
%D 2005
%T Deformation invariant image matching
%A Ling,Haibin
%A Jacobs, David W.
%K deformation
%K deformations;nonaffine
%K deformations;point
%K descriptor;geodesic
%K distances;geodesic
%K geometry;differential
%K geometry;image
%K histogram;image
%K image
%K invariant
%K local
%K matching;computational
%K matching;deformation
%K matching;image
%K morphing;
%K sampling;geodesic-intensity
%X We propose a novel framework to build descriptors of local intensity that are invariant to general deformations. In this framework, an image is embedded as a 2D surface in 3D space, with intensity weighted relative to distance in x-y. We show that as this weight increases, geodesic distances on the embedded surface are less affected by image deformations. In the limit, distances are deformation invariant. We use geodesic sampling to get neighborhood samples for interest points, and then use a geodesic-intensity histogram (GIH) as a deformation invariant local descriptor. In addition to its invariance, the new descriptor automatically finds its support region. This means it can safely gather information from a large neighborhood to improve discriminability. Furthermore, we propose a matching method for this descriptor that is invariant to affine lighting changes. We have tested this new descriptor on interest point matching for two data sets, one with synthetic deformation and lighting change, and another with real non-affine deformations. Our method shows promising matching results compared to several other approaches
%B Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on
%V 2
%P 1466 -1473 Vol. 2 - 1466 -1473 Vol. 2
%8 2005/10//
%G eng
%R 10.1109/ICCV.2005.67
%0 Conference Paper
%B Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
%D 2005
%T A method for converting a smiling face to a neutral face with applications to face recognition
%A Ramachandran, M.
%A Zhou,S. K
%A Jhalani, D.
%A Chellapa, Rama
%K appearance-based
%K Expression
%K Face
%K face;
%K feature
%K invariant
%K motion;
%K neutral
%K nonrigid
%K normalization;
%K recognition;
%K smiling
%X The human face displays a variety of expressions, like smile, sorrow, surprise, etc. All these expressions constitute nonrigid motions of various features of the face. These expressions lead to a significant change in the appearance of a facial image which leads to a drop in the recognition accuracy of a face-recognition system trained with neutral faces. There are other factors like pose and illumination which also lead to performance drops. Researchers have proposed methods to tackle the effects of pose and illumination; however, there has been little work on how to tackle expressions. We attempt to address the issue of expression invariant face-recognition. We present preprocessing steps for converting a smiling face to a neutral face. We expect that this would in turn make the vector in the feature space to be closer to the correct vector in the gallery, in an appearance-based face recognition. This conjecture is supported by our recognition results which demonstrate that the accuracy goes up if we include the expression-normalization block.
%B Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
%V 2
%P ii/977 - ii/980 Vol. 2 - ii/977 - ii/980 Vol. 2
%8 2005/03//
%G eng
%R 10.1109/ICASSP.2005.1415570
%0 Conference Paper
%B Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on
%D 2004
%T Fusion of gait and face for human identification
%A Kale, A.
%A Roy Chowdhury, A.K.
%A Chellapa, Rama
%K access
%K algorithm;
%K analysis;
%K combining
%K control;
%K covert
%K cues;
%K data
%K decision
%K Environment
%K Face
%K fusion;
%K Gait
%K hierarchical
%K holistic
%K human
%K identification;
%K importance
%K intelligent
%K interfaces;
%K invariant
%K perceptual
%K recognition
%K recognition;
%K rules;
%K sampling;
%K score
%K scores;
%K security;
%K sensor
%K sequential
%K similarity
%K view
%X Identification of humans from arbitrary view points is an important requirement for different tasks including perceptual interfaces for intelligent environments, covert security and access control etc. For optimal performance, the system must use as many cues as possible and combine them in meaningful ways. In this paper, we discuss fusion of face and gait cues for the single camera case. We present a view invariant gait recognition algorithm for gait recognition. We employ decision fusion to combine the results of our gait recognition algorithm and a face recognition algorithm based on sequential importance sampling. We consider two fusion scenarios: hierarchical and holistic. The first involves using the gait recognition algorithm as a filter to pass on a smaller set of candidates to the face recognition algorithm. The second involves combining the similarity scores obtained individually from the face and gait recognition algorithms. Simple rules like the SUM, MIN and PRODUCT are used for combining the scores. The results of fusion experiments are demonstrated on the NIST database which has outdoor gait and face data of 30 subjects.
%B Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on
%V 5
%P V - 901-4 vol.5 - V - 901-4 vol.5
%8 2004/05//
%G eng
%R 10.1109/ICASSP.2004.1327257
%0 Conference Paper
%B Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.
%D 2003
%T Towards a view invariant gait recognition algorithm
%A Kale, A.
%A Chowdhury, A.K.R.
%A Chellapa, Rama
%K (access
%K algorithm;
%K analysis;
%K Biometrics
%K biometrics;
%K Calibration
%K calibration;
%K camera
%K canonical
%K control);
%K equations;
%K flow;
%K Gait
%K gait;
%K human
%K image
%K invariant
%K model;
%K MOTION
%K optical
%K perspective
%K phenomenon;
%K projection
%K recognition
%K scheme;
%K sequences;
%K spatio-temporal
%K view
%K view;
%X Human gait is a spatio-temporal phenomenon and typifies the motion characteristics of an individual. The gait of a person is easily recognizable when extracted from a side-view of the person. Accordingly, gait-recognition algorithms work best when presented with images where the person walks parallel to the camera image plane. However, it is not realistic to expect this assumption to be valid in most real-life scenarios. Hence, it is important to develop methods whereby the side-view can be generated from any other arbitrary view in a simple, yet accurate, manner. This is the main theme of the paper. We show that if the person is far enough from the camera, it is possible to synthesize a side view (referred to as canonical view) from any other arbitrary view using a single camera. Two methods are proposed for doing this: (i) using the perspective projection model; (ii) using the optical flow based structure from motion equations. A simple camera calibration scheme for this method is also proposed. Examples of synthesized views are presented. Preliminary testing with gait recognition algorithms gives encouraging results. A by-product of this method is a simple algorithm for synthesizing novel views of a planar scene.
%B Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.
%P 143 - 150
%8 2003/07//
%G eng
%R 10.1109/AVSS.2003.1217914
%0 Conference Paper
%B Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on
%D 1999
%T A rotation, scale and translation resilient public watermark
%A Wu,M.
%A Miller,M.L.
%A Bloom,J.A.
%A Cox,I.J.
%K algorithms;Fourier
%K coding;
%K coding;image
%K data;transform
%K dimensions;projection
%K Fourier-Mellin
%K image
%K invariant
%K methods;detector;experimental
%K of
%K pattern;rotation
%K projection;mapping;original
%K public
%K registration;security
%K resilient
%K results;geometric
%K transform;detection
%K transform;RST
%K transformations;image
%K transforms;image
%K transforms;Radon
%K watermark;RST
%K watermark;scale
%K watermark;translation
%K watermark;watermarking
%K waveform;registration
%X Summary form only given. Watermarking algorithms that are robust to the common geometric transformations of rotation, scale and translation (RST) have been reported for cases in which the original unwatermarked content is available at the detector so as to allow the transformations to be inverted. However, for public watermarks the problem is significantly more difficult since there is no original content to register with. Two classes of solution have been proposed. The first embeds a registration pattern into the content while the second seeks to apply detection methods that are invariant to these geometric transformations. This paper describes a public watermarking method which is invariant (or bares a simple relation) to the common geometric transforms of rotation, scale, and translation. It is based on the Fourier-Mellin transform which has previously been suggested. We extend this work, using a variation based on the Radon transform. The watermark is inserted into a projection of the image. The properties of this projection are such that RST transforms produce simple or no effects on the projection waveform. When a watermark is inserted into a projection, the signal must eventually be back projected to the original image dimensions. This is a one to many mapping that allows for considerable flexibility in the watermark insertion process. We highlight some theoretical and practical issues that affect the implementation of an RST invariant watermark. Finally, we describe preliminary experimental results
%B Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on
%V 4
%P 2065 vol.4 - 2065 vol.4
%8 1999/03//
%G eng
%R 10.1109/ICASSP.1999.758337