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Journal ArticleDOI

Exposing digital forgeries in color filter array interpolated images

Alin C. Popescu, +1 more
- 01 Oct 2005 - 
- Vol. 53, Iss: 10, pp 3948-3959
TLDR
This work quantifies the specific correlations introduced by CFA interpolation, and describes how these correlations can be automatically detected in any portion of an image and shows the efficacy of this approach in revealing traces of digital tampering in lossless and lossy compressed color images interpolated with several different CFA algorithms.
Abstract
With the advent of low-cost and high-resolution digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered. Although good forgeries may leave no visual clues of having been tampered with, they may, nevertheless, alter the underlying statistics of an image. Most digital cameras, for example, employ a single sensor in conjunction with a color filter array (CFA), and then interpolate the missing color samples to obtain a three channel color image. This interpolation introduces specific correlations which are likely to be destroyed when tampering with an image. We quantify the specific correlations introduced by CFA interpolation, and describe how these correlations, or lack thereof, can be automatically detected in any portion of an image. We show the efficacy of this approach in revealing traces of digital tampering in lossless and lossy compressed color images interpolated with several different CFA algorithms.

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Citations
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Determining Image Origin and Integrity Using Sensor Noise

TL;DR: A unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors is provided.
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Image forgery detection

TL;DR: The field of digital forensics has emerged to help restore some trust to digital images and the author reviews the state of the art in this new and exciting field.
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DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

TL;DR: This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations, with special attention to the latest generation of DeepFakes.
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Exposing Digital Forgeries From JPEG Ghosts

TL;DR: A technique to detect whether the part of an image was initially compressed at a lower quality than the rest of the image is described, applicable to images of high and low quality as well as resolution.
Journal ArticleDOI

Region Duplication Detection Using Image Feature Matching

TL;DR: The proposed method starts by estimating the transform between matched scale invariant feature transform (SIFT) keypoints, which are insensitive to geometrical and illumination distortions, and then finds all pixels within the duplicated regions after discounting the estimated transforms.
References
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Book

Pattern classification and scene analysis

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Journal ArticleDOI

Cubic convolution interpolation for digital image processing

TL;DR: It can be shown that the order of accuracy of the cubic convolution method is between that of linear interpolation and that of cubic splines.
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