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Photo fakery nabbed via outsmarting techniques

Qualitative results for multi-class image manipulation detection on NIST16 dataset. RGB and noise map provide different information for splicing, copy-move and removal. By combining the features from the RGB image with the noise features, RGB-N produces the correct classification for different tamepring techniques. Credit: Peng Zhou et al.

Adobe Research has been getting busy nailing down how to spot image manipulations by unleashing AI on the case. In doing so, they may be achieving real headway in the field of image forensics.

You can check out the paper, "Learning Rich Features for Image Manipulation Detection," by authors whose affiliations include Adobe Research and University of Maryland, College Park.

Please click the image below to read the entire article.

Source: Tech Xplore

James Zjalic