Training super-recognisers’ detection and discrimination of computer-generated faces

Abstract excerpt

<p>Generative Adversarial Networks (GANs) can create realistic synthetic faces, which have the potential to be used for nefarious purposes. The synthetic faces produced by GANs are difficult to detect and are often judged to be more realistic than real faces. Training programmes have been developed to improve human GAN detection accuracy, with mixed results. Here we investigate GAN face detection and discriminatio...

Topics

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• Algorithms
• Child
• Child, Preschool
• Face
• Image Processing, Computer-Assisted
• Infant
• Infant, Newborn
• Machine Learning

Identifiers and source

Literature Corpus work

6cb1fb03-4b13-5676-84cd-7d2e52ea7333

DOI

10.31234/osf.io/5jqh8_v2