  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. Code and models are available
at https://keshik6.github.io/transferable-forensic-features/
