Colour Processing in Adversarial Attacks on Face Liveness Systems

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dc.contributor.authorAbduh, Latifahen_US
dc.contributor.authorIvrissimtzis, Ioannisen_US
dc.contributor.editorVidal, Franck P. and Tam, Gary K. L. and Roberts, Jonathan C.en_US
dc.date.accessioned2019-09-11T05:09:12Z
dc.date.available2019-09-11T05:09:12Z
dc.date.issued2019
dc.description.abstractIn the context of face recognition systems, liveness test is a binary classification task aiming at distinguishing between input images that come from real people's faces and input images that come from photos or videos of those faces, and presented to the system's camera by an attacker. In this paper, we train the state-of-the-art, general purpose deep neural network ResNet for liveness testing, and measure the effect on its performance of adversarial attacks based on the manipulation of the saturation component of the imposter images. Our findings suggest that higher saturation values in the imposter images lead to a decrease in the network's performance. Next, we study the relationship between the proposed adversarial attacks and corresponding direct presentation attacks. Initial results on a small dataset of processed images which are then printed on paper or displayed on an LCD or a mobile phone screen, show that higher saturation values lead to higher values in the network's loss function, indicating that these colour manipulation techniques can indeed be converted into enhanced presentation attacks.en_US
dc.description.sectionheadersShort Papers
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20191272
dc.identifier.isbn978-3-03868-096-3
dc.identifier.pages149-152
dc.identifier.urihttps://doi.org/10.2312/cgvc.20191272
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20191272
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectComputer vision tasks
dc.subjectImage manipulation
dc.titleColour Processing in Adversarial Attacks on Face Liveness Systemsen_US
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