Detecting Digital Forgeries Using Bispectral Analysis
Detecting Digital Forgeries Using Bispectral Analysis
Exposing digital forgeries in scientific images
MM&Sec '06 Proceedings of the 8th workshop on Multimedia and security
Space-Time Super-Resolution Using Graph-Cut Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical tools for digital forensics
IH'04 Proceedings of the 6th international conference on Information Hiding
Exposing digital forgeries by detecting traces of resampling
IEEE Transactions on Signal Processing
Detection of doctored images using correlations of PSF
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Detection of doctored images using correlations of PSF
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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We address the problem of detection of image doctoring using correlations of Point Spread Function (PSF) and iterative blind deconvolution. Doctoring is a process of tampering or hampering or changing the content of an image in order to deceive people or rewrite history or exaggerate the situations or customize ground-breaking advances in research, etc. We propose a method to detect a given image is doctored or original. We present an unified framework which uses a generative model of the imaging process and can address the problem of detection of doctoring. Doctoring process is modeled as the convolution of original image with the nonlinear filter used for generating the doctored image. The characteristics of the authentic image from the given imaging model are used to facilitate the detection of the doctored images. The correlation pattern of the estimated PSF is used to detect the doctoring in an image. We demonstrate the proposed algorithm on different doctored images, which includes doctoring using splicing, cloning and re-touching. On an average, we achieve a detection rate of 74% for doctored images generated with different doctoring methods.