The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective
Keywords: adversarial robustness, flatness, LLMs
TL;DR: We find that adversarial examples lie in dense uncanny valleys.
Abstract: Flatness of the loss surface not only correlates positively with generalization, but is also related to adversarial
robustness, since perturbations of inputs relate non-linearly to perturbations of weights. In this paper, we empirically
analyze the relation between adversarial examples and relative flatness with respect to the parameters of one layer.
We observe a peculiar property of adversarial examples in the context of relative flatness: during an iterative first-order
white-box attack, the flatness of the loss surface measured around the adversarial example *first* becomes sharper
until the label is flipped, but if we keep the attack running, it runs into a flat *uncanny valley* where the label remains
flipped. In extensive experiments, we observe this phenomenon across various model architectures and datasets,
even for adversarially trained models. Our results also extend to large language models (LLMs), but due to the discrete
nature of the input space and comparatively weak attacks, adversarial examples rarely reach truly flat regions. Most
importantly, this phenomenon shows that flatness alone cannot explain adversarial robustness unless we can also
guarantee the behavior of the function around the examples. We therefore theoretically connect relative flatness to
adversarial robustness by bounding the third derivative of the loss surface, underlining the need for flatness in
combination with a low global Lipschitz constant for a robust model.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 9646
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