  It is well known that a determined adversary can fool a neural network by
making imperceptible adversarial perturbations to an image. Recent studies have
shown that these perturbations can be detected even without information about
the neural network if the strategy taken by the adversary is known beforehand.
Unfortunately, these studies suffer from the generalization limitation -- the
detection method has to be recalibrated every time the adversary changes his
strategy. In this study, we attempt to overcome the generalization limitation
by deriving a metric which reliably identifies adversarial images even when the
approach taken by the adversary is unknown. Our metric leverages key
differences between the spectra of clean and adversarial images when an image
is treated as a matrix. Our metric is able to detect adversarial images across
different datasets and attack strategies without any additional re-calibration.
In addition, our approach provides geometric insights into several unanswered
questions about adversarial perturbations.
