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Presenter: Alexander Binder, University of Oslo and Singapore Institute of Technology
Abstract: Visual counterfeits are increasingly causing an existential conundrum in mainstream media with rapid evolution in neural image synthesis methods. Though detection of such counterfeits has been a taxing problem in the image forensics community, a recent class of forensic detectors – universal detectors – are able to surprisingly spot counterfeit images regardless of generator architectures, loss functions, training datasets, and resolutions.This intriguing property suggests the possible existence of transferable forensic features (T-FF) in universal detectors. In this work, we conduct the first analytical study to discover and understand T-FF in universal detectors.Our contributions are 2-fold: 1)We propose a novel forensic feature relevance statistic (FF-RS) to quantify and discover T-FF in universal detectors and, 2)Our qualitative and quantitative investigations uncover an unexpected finding:color is a critical T-FF in universal detectors.
The seminar is on a recently accepted paper. More information can be found here.
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