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IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING

Citation Author(s):
Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho
Submitted by:
Tiago Carvalho
Last updated:
7 October 2018 - 5:48am
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Tiago Carvalho
Paper Code:
2085
Categories:
 

Fake news and deep fakes have been making social and mainstream media headlines. At the same time, engaged scientists strive for find- ing ways to detect forgeries and suspicious manipulations using even the subtlest clues. In this vein, this work proposes a new method for detecting photographic splicing by bringing together the high repre- sentation power of Illuminant Maps and Convolutional Neural Net- works as a way of learning directly from available training data the most important hints of a forgery. This work propose a method that eliminates the laborious feature engineering process, allow locate forgery region and yields a classification accuracy of more than 96%, outperforming state-of-the-art methods in different datasets. The po- tential uses of the proposed method is further highlighted by analyz- ing some suspicious real-world photographs that recently broke the news

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