A general metric for identifying adversarial images
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.
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Siddharth Krishna Kumar (add twitter)
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07/29/18 05:54PM
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arxiv_org: A general metric for identifying adversarial images. https://t.co/8bHsk1tmVa https://t.co/IhfC9t3rOO
nmfeeds: [O] https://t.co/yFlmwEaCHt A general metric for identifying adversarial images. It is well known that a determined advers...
nmfeeds: [CV] https://t.co/yFlmwEaCHt A general metric for identifying adversarial images. It is well known that a determined adver...
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