Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Blind passive media forensics: motivation and opportunity
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Exposing digital forgeries in color filter array interpolated images
IEEE Transactions on Signal Processing - Part II
Steganalysis using higher-order image statistics
IEEE Transactions on Information Forensics and Security
Digital camera identification from sensor pattern noise
IEEE Transactions on Information Forensics and Security
Digital Single Lens Reflex Camera Identification From Traces of Sensor Dust
IEEE Transactions on Information Forensics and Security
Blind Identification of Source Cell-Phone Model
IEEE Transactions on Information Forensics and Security
Determining Image Origin and Integrity Using Sensor Noise
IEEE Transactions on Information Forensics and Security
Digital Image Forensics via Intrinsic Fingerprints
IEEE Transactions on Information Forensics and Security
Nonintrusive Component Forensics of Visual Sensors Using Output Images
IEEE Transactions on Information Forensics and Security
Steganalysis using image quality metrics
IEEE Transactions on Image Processing
A bibliography on blind methods for identifying image forgery
Image Communication
Building fingerprints with information from three color bands for source camera identification
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
Digital image forensics: a booklet for beginners
Multimedia Tools and Applications
Passive detection of paint-doctored JPEG images
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
Detection of tampering inconsistencies on mobile photos
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
CFA pattern identification of digital cameras using intermediate value counting
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
Measuring the statistical correlation inconsistencies in mobile images for tamper detection
Transactions on Data Hiding and Multimedia Security VII
Camera model identification based on the characteristic of CFA and interpolation
IWDW'11 Proceedings of the 10th international conference on Digital-Forensics and Watermarking
Decomposed PRNU Library for Forensics on Photos
International Journal of Digital Library Systems
A Survey of Digital Forensic Techniques for Digital Libraries
International Journal of Digital Library Systems
Robustness of color interpolation identification against anti-forensic operations
IH'12 Proceedings of the 14th international conference on Information Hiding
Hi-index | 0.00 |
In this paper, we propose a novel accurate detection framework of demosaicing regularity from different source images. The proposed framework first reversely classifies the demosaiced samples into several categories and then estimates the underlying demosaicing formulas for each category based on partial second-order derivative correlation models, which detect both the intrachannel and the cross-channel demosaicing correlation. An expectation-maximization reverse classification scheme is used to iteratively resolve the ambiguous demosaicing axes in order to best reveal the implicit grouping adopted by the underlying demosaicing algorithm. Comparison results based on syntactic images show that our proposed formulation significantly improves the accuracy of the regenerated demosaiced samples from the sensor samples for a large number of diversified demosaicing algorithms. By running sequential forward feature selection, our reduced feature sets used in conjunction with the probabilistic support vector machine classifier achieve superior performance in identifying 16 demosaicing algorithms in the presence of common camera postdemosaicing processing. When applied to real applications, including camera model and RAW-tool identification, our selected features achieve nearly perfect classification performances based on large sets of cropped image blocks.