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Black-box Adversarial Attacks with Limited Queries and Information
Andrew Ilyas · Logan Engstrom · Anish Athalye · Jessy Lin

Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #106

Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model. In practice, the threat model for real-world systems is often more restrictive than the typical black-box model where the adversary can observe the full output of the network on arbitrarily many chosen inputs. We define three realistic threat models that more accurately characterize many real-world classifiers: the query-limited setting, the partial-information setting, and the label-only setting. We develop new attacks that fool classifiers under these more restrictive threat models, where previous methods would be impractical or ineffective. We demonstrate that our methods are effective against an ImageNet classifier under our proposed threat models. We also demonstrate a targeted black-box attack against a commercial classifier, overcoming the challenges of limited query access, partial information, and other practical issues to break the Google Cloud Vision API.

Author Information

Andrew Ilyas (Massachusetts Institute of Technology)
Logan Engstrom (MIT)
Anish Athalye (MIT CSAIL)
Jessy Lin (MIT)

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