Real or Fake? Spoofing State-Of-The-Art Face Synthesis Detection Systems
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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, which raises obvious concerns about the potential for misuse. These concerns have fostered the research of manipulation detection methods that, contrary to humans, have already achieved astonishing results in some scenarios. In this study, we focus on the entire face synthesis, which is one specific type of facial manipulation. The main contributions of this study are: i) a novel strategy to remove GAN "fingerprints" from synthetic fake images in order to spoof facial manipulation detection systems, while keeping the visual quality of the resulting images, ii) an in-depth analysis of state-of-the-art detection approaches for the entire face synthesis manipulation, iii) a complete experimental assessment of this type of facial manipulation considering state-of-the-art detection systems, remarking how challenging is this task in unconstrained scenarios, and finally iv) a novel public database named FSRemovalDB produced after applying our proposed GAN-fingerprint removal approach to original synthetic fake images. The observed results led us to conclude that more efforts are required to develop robust facial manipulation detection systems against unseen conditions and spoof techniques such as the one proposed in this study.
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