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Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization

Citation Author(s):
Joel Brogan, Paolo Bestagini, Aparna Bharati, Allan Pinto, Daniel Moreira, Kevin Bowyer, Patrick Flynn, Anderson Rocha, Walter Scheirer
Submitted by:
Joel Brogan
Last updated:
14 September 2017 - 10:48pm
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Joel Brogan
Paper Code:
2523
 

As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows. In this paper, we revisit the ideas of image querying and retrieval to provide clues to better localize forgeries. We propose a method to perform large-scale image forensics on the order of one million images using the help of an image search algorithm and database to gather contextual clues as to where tampering may have taken place. In this vein, we introduce five new strongly invariant image comparison methods and test their effectiveness under heavy noise, rotation, and color space changes. Lastly, we show the effectiveness of these methods compared to passive image forensics using Nimble, a new, state-of-the-art dataset from the National Institute of Standards and Technology (NIST).

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