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Understanding adversarial examples requires a theory of artefacts for deep learning


Deep neural networks are currently the most widespread and successful technology in artificial intelligence. However, these systems exhibit bewildering new vulnerabilities: most notably a susceptibility to adversarial examples. Here, I review recent empirical research on adversarial examples that suggests that deep neural networks may be detecting in them features that are predictively useful, though inscrutable to humans. To understand the implications of this research, we should contend with some older philosophical puzzles about scientific reasoning, helping us to determine whether these features are reliable targets of scientific investigation or just the distinctive processing artefacts of deep neural networks.

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Fig. 1: An ‘impersonation’ attack using ‘adversarial glasses’.
Fig. 2: A periodic signal function approximated by a Fourier series.
Fig. 3: Checkerboard artefacts produced by image deconvolution in GANs.

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This work has been supported by National Science Foundation grant 2020585.

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Correspondence to Cameron Buckner.

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Buckner, C. Understanding adversarial examples requires a theory of artefacts for deep learning. Nat Mach Intell 2, 731–736 (2020).

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