A Computational Approach to Edge Detection
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
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Detecting Doctored Images Using Camera Response Normality and Consistency
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Detection of Region-Duplication Forgery in Digital Image
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Evaluation of Features Detectors and Descriptors based on 3D Objects
International Journal of Computer Vision
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm
PACIIA '08 Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 02
A bibliography on blind methods for identifying image forgery
Image Communication
WLD: A Robust Local Image Descriptor
IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Region duplication detection using image feature matching
IEEE Transactions on Information Forensics and Security
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
MIFT: a mirror reflection invariant feature descriptor
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery
IEEE Transactions on Information Forensics and Security - Part 2
Hi-index | 0.00 |
Copy---move image forgery detection has recently become a very active research topic in blind image forensics. In copy---move image forgery, a region from some image location is copied and pasted to a different location of the same image. Typically, post-processing is applied to better hide the forgery. Using keypoint-based features, such as SIFT features, for detecting copy---move image forgeries has produced promising results. The main idea is detecting duplicated regions in an image by exploiting the similarity between keypoint-based features in these regions. In this paper, we have adopted keypoint-based features for copy---move image forgery detection; however, our emphasis is on accurate and robust localization of duplicated regions. In this context, we are interested in estimating the transformation (e.g., affine) between the copied and pasted regions more accurately as well as extracting these regions as robustly by reducing the number of false positives and negatives. To address these issues, we propose using a more powerful set of keypoint-based features, called MIFT, which shares the properties of SIFT features but also are invariant to mirror reflection transformations. Moreover, we propose refining the affine transformation using an iterative scheme which improves the estimation of the affine transformation parameters by incrementally finding additional keypoint matches. To reduce false positives and negatives when extracting the copied and pasted regions, we propose using "dense" MIFT features, instead of standard pixel correlation, along with hysteresis thresholding and morphological operations. The proposed approach has been evaluated and compared with competitive approaches through a comprehensive set of experiments using a large dataset of real images (i.e., CASIA v2.0). Our results indicate that our method can detect duplicated regions in copy---move image forgery with higher accuracy, especially when the size of the duplicated region is small.