Nov 26, 2023 · This paper shows that adversarially trained models are vulnerable to a new threat called two-faced attacks, where slight perturbations in input features are ...
In this paper, we disclose that adversarially trained models are vulnerable to two-faced attacks, where slight perturbations in input features are crafted ...
Poster. On the Vulnerability of Adversarially Trained Models Against Two-faced Attacks. Shengjie Zhou · Lue Tao · Yuzhou Cao · Tao Xiang · Bo An · Lei Feng.
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Jul 21, 2022 · sarial examples through adversarial training may increase the vulnerability of models against extraction attacks. This proves that following ...
Sep 9, 2024 · In this blog, we will explore why machine learning models are vulnerable to adversarial attacks, the different types of adversarial attacks, the ...
Mar 6, 2024 · We propose two simple, computationally cheap vulnerability-aware reweighting functions for assigning perturbation bounds to adversarial examples used for AT.
Sep 27, 2021 · In the following parts, we show that the trained two deep learning models would achieve bad performance against generated adversarial examples.
In this paper, we study fast training of adversarially ro- bust models. From the analyses of the state-of-the-art de- fense method, i.e., the multi-step ...
We propose a Remix method with multiple input transformations, which could achieve multiple data augmentation by utilizing gradients from previous iterations.
Jul 19, 2024 · This paper explores these vulnerabilities, attributing them to the complexity of neural networks, the diversity of training data, and the ...