Construct · 1 fields · 1 citations

transferability of adversarial examples: a standalone construct

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Adversarial machine learning

adversarial inputs crafted against one model often fool other models trained separately, enabling black-box attacks.

In adversarial machine learning, transferability of adversarial examples refers to adversarial inputs crafted against one model often fool other models trained separately, enabling black-box attacks. This meaning comes from Papernot (2016) in arXiv.[1]

Unit
Recovery means
Measured by

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Nearby constructs

learning6 fields · 14 citationsjingle · one name, six constructs model3 fields · 3 citationsjingle · one word, three constructs transferability1 fields · 1 citationsanchor · a standalone construct

References

Papernot, N., McDaniel, P., & Goodfellow, I. (2016). Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples. arXiv. https://doi.org/10.48550/arxiv.1605.07277

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