Kevin Bernardo

TL;DR: A differential facial retouching detection system is proposed which processes pairs of a potentially retouched reference image and corresponding unaltered probe image of single subjects and is shown to outperform several state-of-the-art single image-based detection schemes.

TL;DR: Results obtained from challenging cross-database experiments in which the analyzed retouching technique is unknown during training yield interesting findings, including the finding that in some cases, the application of image compression might as well improve detection performance.

TL;DR: A simulation of print-scan transformations for face images based on a Conditional Generative Adversarial Network (cGAN) is proposed, where subsets of two public face databases are manually printed and scanned using different printer-scanner combinations.

TL;DR: In this paper, the authors presented a new face database consisting of 400 pairs of doppelganger images and evaluated two state-of-the-art face recognition systems on said database and other public datasets, including the Disguised Faces in The Wild (DFW) database.

TL;DR: In this article, the effects of image compression on face image manipulation detection are analyzed and novel detection algorithms utilizing texture descriptors and deep face representations are proposed and evaluated in a single image and differential scenario.