On describing complex surface shapes
Image and Vision Computing
Elements of information theory
Elements of information theory
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Real-Time Rendering
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
Visually Searching the Web for Content
IEEE MultiMedia
The Nonlinear Statistics of High-Contrast Patches in Natural Images
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Unsupervised Image Translation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting cartoons: a case study in automatic video-genre classification
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
How realistic is photorealistic?
IEEE Transactions on Signal Processing
A general framework for low level vision
IEEE Transactions on Image Processing
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Semi Supervised Image Spam Hunter: A Regularized Discriminant EM Approach
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
An Enhanced Statistical Approach to Identifying Photorealistic Images
IWDW '09 Proceedings of the 8th International Workshop on Digital Watermarking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A bibliography on blind methods for identifying image forgery
Image Communication
Forensic detection of image manipulation using statistical intrinsic fingerprints
IEEE Transactions on Information Forensics and Security
Discriminating computer graphics images and natural images using hidden Markov tree model
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
A smart phone image database for single image recapture detection
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
Vision of the unseen: Current trends and challenges in digital image and video forensics
ACM Computing Surveys (CSUR)
A passive image authentication scheme for detecting region-duplication forgery with rotation
Journal of Network and Computer Applications
NPIC: hierarchical synthetic image classification using image search and generic features
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Homomorphic signatures for digital photographs
FC'11 Proceedings of the 15th international conference on Financial Cryptography and Data Security
Classification of digital camera-models based on demosaicing artifacts
Digital Investigation: The International Journal of Digital Forensics & Incident Response
IWDW'11 Proceedings of the 10th international conference on Digital-Forensics and Watermarking
Distinguishing computer graphics from photographic images using local binary patterns
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
Journal of Visual Communication and Image Representation
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The increasing photorealism for computer graphics has made computer graphics a convincing form of image forgery. Therefore, classifying photographic images and photorealistic computer graphics has become an important problem for image forgery detection. In this paper, we propose a new geometry-based image model, motivated by the physical image generation process, to tackle the above-mentioned problem. The proposed model reveals certain physical differences between the two image categories, such as the gamma correction in photographic images and the sharp structures in computer graphics. For the problem of image forgery detection, we propose two levels of image authenticity definition, i.e., imaging-process authenticity and scene authenticity, and analyze our technique against these definitions. Such definition is important for making the concept of image authenticity computable. Apart from offering physical insights, our technique with a classification accuracy of 83.5% outperforms those in the prior work, i.e., wavelet features at 80.3% and cartoon features at 71.0%. We also consider a recapturing attack scenario and propose a counter-attack measure. In addition, we constructed a publicly available benchmark dataset with images of diverse content and computer graphics of high photorealism.