Transcription of Practical Black-Box Attacks against Machine Learning
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Practical Black-Box Attacks against Machine Learning
Practical Black-Box Attacks against Machine LearningNicolas PapernotPennsylvania State McDanielPennsylvania State Goodfellow JhaUniversity of Berkay CelikPennsylvania State SwamiUS Army Research Learning (ML) models, , deep neural networks(DNNs), are vulnerable to adversarial examples: maliciousinputs modified to yield erroneous model outputs, while ap-pearing unmodified to human observers. Potential attacksinclude having malicious content like malware identified aslegitimate or controlling vehicle behavior. Yet, all existingadversarial example Attacks require knowledge of either themodel internals or its training data. We introduce the firstpractical demonstration of an attacker controlling a remotelyhosted DNN with no such knowledge.
to the benign or malware class. E orts in the security [5, 2, 9, 18] and machine learning [14, 4] communities exposed the Work done while the author was at Google. Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government.
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