Face Recognition System Using Local Autocorrelations and Multiscale Integration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition using higher-order local autocorrelation coefficients
Pattern Recognition Letters
Exposing digital forgeries by detecting inconsistencies in lighting
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
An Effective Algorithm of Image Splicing Detection
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
Steganalysis Versus Splicing Detection
IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
Image Splicing Detection Using Camera Characteristic Inconsistency
MINES '09 Proceedings of the 2009 International Conference on Multimedia Information Networking and Security - Volume 01
Statistical tools for digital forensics
IH'04 Proceedings of the 6th international conference on Information Hiding
IWDW'06 Proceedings of the 5th international conference on Digital Watermarking
Exposing digital forgeries by detecting traces of resampling
IEEE Transactions on Signal Processing
How realistic is photorealistic?
IEEE Transactions on Signal Processing
Exposing digital forgeries in color filter array interpolated images
IEEE Transactions on Signal Processing - Part II
Digital image splicing detection based on Markov features in DCT and DWT domain
Pattern Recognition
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With the advance of digitization and digital processing techniques, digital images are now easy to create and manipulate, and leave no clues of artificial evidence. There are some known digital fakery for images, e.g., computer graphics (CGs) and digital forgeries. As valid records of natural world, natural images, i.e., photographic images, are no longer believable. In this paper, a detection scheme for natural images and fake images is proposed. Features are first extracted using multiresolution decomposition and higher order local autocorrelations (HLACs). The support vector machines (SVMs) are then used to differentiate the natural and fake images. Because the inner product between features can be obtained directly without computing features, it can be integrated into SVM, and the computation complexity is decreased. Experiments show that the proposed detection scheme is effective, demonstrating that the proposed statistical features can model the differences between natural images and fake images.