An experimental evaluation of computer graphics imagery
ACM Transactions on Graphics (TOG)
Measuring the Perception of Visual Realism in Images
Proceedings of the 12th Eurographics Workshop on Rendering Techniques
The Nonlinear Statistics of High-Contrast Patches in Natural Images
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Exploring perceptual equivalence between real and simulated imagery
APGV '05 Proceedings of the 2nd symposium on Applied perception in graphics and visualization
Physics-motivated features for distinguishing photographic images and computer graphics
Proceedings of the 13th annual ACM international conference on Multimedia
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
How realistic is photorealistic?
IEEE Transactions on Signal Processing
Digital Image Forensics via Intrinsic Fingerprints
IEEE Transactions on Information Forensics and Security
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Differentiating computer graphics from natural images remains a representative problem of digital image forensics because the two categories of images reflect typical different aspects of generation and forgery of digital images. This paper aims to address this problem through analyzing the statistical property of local edge patches in digital images. First, we preprocess image edge patches and project them into a 7-dimensional sphere as in [7]. Then, a visual vocabulary is constructed via determining the key sampling points in accordance with Voronoi cells. The proposed approach to constructing visual vocabulary avoids troubles in traditional partitioning algorithms such as k-means. And then, a given image is represented as a binned histogram of visual words and the corresponding feature vector is formed by the bins. Finally, we employ an SVM classifier for image classification. Our experimental results demonstrate the efficient discrimination of the proposed features.