Title :
Accurate detection of demosaicing regularity from output images
Author :
Cao, Hong ; Kot, Alex C.
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
Abstract :
Demosaicing regularity is an important processing regularity associated with the internal camera processing and its detection from output photos is useful for non-intrusive forensic engineering. In this paper, we propose a reverse grouping technique to improve the detection accuracy of our earlier proposed detection model based on second-order image derivatives. Comparison results based on syntactic images shows that the proposed technique significantly reduces the reprediction errors for some commonly used demosaicing algorithms. When applied to a real application, i.e. camera model identification, our demosaicing features in conjunction with probabilistic support vector machine classifier achieve excellent classification performance.
Keywords :
image classification; image reconstruction; image segmentation; probability; security of data; support vector machines; camera model identification; demosaicing regularity detection; image reconstruction; internal camera processing; nonintrusive forensic engineering; output image; probabilistic support vector machine classifier; reverse grouping technique; second-order image derivative; syntactic image; Digital cameras; Digital images; Filtering; Forensics; Image coding; Optical distortion; Optical filters; Sensor phenomena and characterization; Transform coding; Watermarking;
Conference_Titel :
Circuits and Systems, 2009. ISCAS 2009. IEEE International Symposium on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-3827-3
Electronic_ISBN :
978-1-4244-3828-0
DOI :
10.1109/ISCAS.2009.5117794