The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
The Nonlinear Statistics of High-Contrast Patches in Natural Images
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Physics-motivated features for distinguishing photographic images and computer graphics
Proceedings of the 13th annual ACM international conference on Multimedia
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Can we trust digital image forensics?
Proceedings of the 15th international conference on Multimedia
Detecting Photographic Composites of People
IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
A Study of Color Histogram Based Image Retrieval
ITNG '09 Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations
IEEE Transactions on Multimedia
Progressive randomization: Seeing the unseen
Computer Vision and Image Understanding
Multi-scale binary patterns for texture analysis
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Texture features corresponding to human touch feeling
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Robust fusion: extreme value theory for recognition score normalization
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Vision of the unseen: Current trends and challenges in digital image and video forensics
ACM Computing Surveys (CSUR)
Full length article: Compactly supported shearlets are optimally sparse
Journal of Approximation Theory
How realistic is photorealistic?
IEEE Transactions on Signal Processing
A new class of two-channel biorthogonal filter banks and waveletbases
IEEE Transactions on Signal Processing
Exposing digital forgeries in color filter array interpolated images
IEEE Transactions on Signal Processing - Part II
Digital camera identification from sensor pattern noise
IEEE Transactions on Information Forensics and Security
A general framework for low level vision
IEEE Transactions on Image Processing
Image compression via joint statistical characterization in the wavelet domain
IEEE Transactions on Image Processing
The finite ridgelet transform for image representation
IEEE Transactions on Image Processing
Face spoofing detection through partial least squares and low-level descriptors
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
Real or Fake?: human judgments about photographs and computer-generated images of faces
SIGGRAPH Asia 2012 Technical Briefs
Video-Based Face Spoofing Detection through Visual Rhythm Analysis
SIBGRAPI '12 Proceedings of the 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images
Hi-index | 0.00 |
The development of powerful and low-cost hardware devices allied with great advances on content editing and authoring tools have promoted the creation of computer generated images (CG) to a degree of unrivaled realism. Differentiating a photo-realistic computer generated image from a real photograph (PG) can be a difficult task to naked eyes. Digital forensics techniques can play a significant role in this task. As a matter of fact, important research has been made by the scientific community in this regard. Most of the approaches focus on single image features aiming at detecting differences between real and computer generated images. However, with the current technology advances, there is no universal image characterization technique that completely solves this problem. In our work, we (1) present a complete study of several CG versus PG approaches; (2) create a large and heterogeneous dataset to be used as a training and validation database; (3) implement representative methods of the literature; and (4) devise automatic ways for combining the best approaches. We compared the implemented methods using the same validation environment showing their pros and cons with a common benchmark protocol. We collected approximately 4850 photographs and 4850 CGs with large diversity of image content and quality. We implemented a total of 13 methods. Results show that this set of methods can achieve up to 93% of accuracy when used without any form of machine learning fusion. The same methods, when combined through the implemented fusion schemes, can achieve an accuracy rate of 97%, representing a reduction of 57% of the classification error over the best individual result.