Papers › An Analysis of Robustness of Non-Lipschitz Networks

An Analysis of Robustness of Non-Lipschitz Networks

13 Oct 2020arXiv:2010.06154archive 2025-07-28

Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang

Despite significant advances, deep networks remain highly susceptible to adversarial attack. One fundamental challenge is that small input perturbations can often produce large movements in the network's final-layer feature space. In this paper, we define an attack model that abstracts this challenge, to help understand its intrinsic properties. In our model, the adversary may move data an arbitrary distance in feature space but only in random low-dimensional subspaces. We prove such adversaries can be quite powerful: defeating any algorithm that must classify any input it is given. However, by allowing the algorithm to abstain on unusual inputs, we show such adversaries can be overcome when classes are reasonably well-separated in feature space. We further provide strong theoretical guarantees for setting algorithm parameters to optimize over accuracy-abstention trade-offs using data-driven methods. Our results provide new robustness guarantees for nearest-neighbor style algorithms, and also have application to contrastive learning, where we empirically demonstrate the ability of such algorithms to obtain high robust accuracy with low abstention rates. Our model is also motivated by strategic classification, where entities being classified aim to manipulate their observable features to produce a preferred classification, and we provide new insights into that area as well.

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load_data dravyanshsharma/adversarial-contrastive/n3_1__optimal_attack.py official repository ran · our draft was wrong MIT (permissive) · 50768e5c6de22234 · report
make_rand_vector dravyanshsharma/adversarial-contrastive/n3_1__optimal_attack.py official repository ran · honoured contract fingerprinted MIT (permissive) · 14668e20fe120e68 · report
rotation_matrix dravyanshsharma/adversarial-contrastive/n3_1__optimal_attack.py official repository ran · honoured contract fingerprinted MIT (permissive) · d936aa6a51d78d60 · report

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Adversarial AttackAdversarial DefenseAdversarial RobustnessContrastive LearningDecision Making

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