Oct 26, 2023 · An intriguing property of this phenomenon is the transferability of adversarial examples, where perturbations crafted for one model can deceive ...
This survey explores the landscape of the adversarial transferability of adversarial examples. We categorize existing methodologies to enhance adversarial ...
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May 2, 2024 · While the existence of adversarial examples has raised concerns about the robustness and reliability of ma- chine learning systems, researchers ...
This repo lists relevant papers summarized in our survey paper. A Survey on Transferability of Adversarial Examples across Deep Neural Networks.
Apr 26, 2024 · Please check our survey paper on adversarial transferability across deep neural networks, which has just been accepted in TMLR .
Deep neural networks are vulnerable to adversarial ex- amples (AEs), which have adversarial transferability: AEs generated for the source model can mislead ...
This work empirically investigate two classes of factors that might influence the transferability of adversarial examples, and proposes a simple butective ...
6 days ago · Our paper introduces a ranking strategy that refines the transfer attack process, enabling the attacker to estimate the likelihood of success ...
studied the transferability between conventional machine learning tech- niques (i.e., logistic regression, SVM, decision tree, kNN) and deep neural networks.
We propose a FIM to generate transferable adversarial examples using multi-features. •. This FIM improves the adversarial capability of AEs across deep task ...
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