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GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection by Neves, João C.; Tolosana, Ruben; Vera-Rodriguez, Ruben; Lopes, Vasco; Proença, Hugo; Fierrez, Julian is a scholarly article available to read on EtoBox.

What is GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection about?

The availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse. Such concerns have fostered the research on manipulation detection methods that, contrary to humans, have already achieved astonishing results in various scenarios. In this study, we focus on the synthesis of entire facial images, which is a specific type of facial manipulation. The main contributions of this study are four-fold: i) a novel strategy to remove GAN "fingerprints" from synthetic fake images based on autoencoders is described, in order to spoof facial manipulation detection systems while keeping the visual quality of the resulting images; ii) an in-depth analysis of the recent literature in facial manipulation detection; iii) a complete experimental assessment of this type of facial manipulation, considering the state-of-the-art fake detection systems (based on holistic deep networks, steganalysis, and local artifacts), remarking how challenging is this task in unconstrai

Author

Neves, João C.; Tolosana, Ruben; Vera-Rodriguez, Ruben; Lopes, Vasco; Proença, Hugo; Fierrez, Julian

Published

2019

Language

EN