On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
A survey of watermarking algorithms for image authentication
EURASIP Journal on Applied Signal Processing
A natural image model approach to splicing detection
Proceedings of the 9th workshop on Multimedia & security
Fusion Based Blind Image Steganalysis by Boosting Feature Selection
IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
Run-Length and Edge Statistics Based Approach for Image Splicing Detection
Digital Watermarking
A bibliography on blind methods for identifying image forgery
Image Communication
Region duplication detection using image feature matching
IEEE Transactions on Information Forensics and Security
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Revealing digital fakery using multiresolution decomposition and higher order statistics
Engineering Applications of Artificial Intelligence
Digital image splicing detection based on approximate run length
Pattern Recognition Letters
IWDW'06 Proceedings of the 5th international conference on Digital Watermarking
A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery
IEEE Transactions on Information Forensics and Security - Part 2
Hi-index | 0.01 |
Image splicing is very common and fundamental in image tampering. To recover people's trust in digital images, the detection of image splicing is in great need. In this paper, a Markov based approach is proposed to detect this specific artifact. Firstly, the original Markov features generated from the transition probability matrices in DCT domain by Shi et al. is expanded to capture not only the intra-block but also the inter-block correlation between block DCT coefficients. Then, more features are constructed in DWT domain to characterize the three kinds of dependency among wavelet coefficients across positions, scales and orientations. After that, feature selection method SVM-RFE is used to fulfill the task of feature reduction, making the computational cost more manageable. Finally, support vector machine (SVM) is exploited to classify the authentic and spliced images using the final dimensionality-reduced feature vector. The experiment results demonstrate that the proposed approach can outperform some state-of-the-art methods.