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Towards a Novel Perspective on Adversarial Examples Driven by Frequency. Enhancing our understanding of adversarial examples is crucial for the secure application of machine learning models in real-world scenarios. A prevalent method for analyzing adversarial examples is through a frequency-based approach.
Apr 16, 2024
Apr 16, 2024 · In this paper, we seek to demystify this relationship by exploring the characteristics of adversarial perturbations within the frequency domain.
Nov 29, 2024 · Bibliographic details on Towards a Novel Perspective on Adversarial Examples Driven by Frequency.
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An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and ...
Specifically, we design a method to detect adversarial examples using frequency domain reconstruction. After converting the input adversarial examples into the ...
Jan 28, 2022 · Our analysis shows that adversarial examples are neither in high-frequency nor in low-frequency components, but are simply dataset dependent.
We propose a novel adversarial attack, SSA, which is applicable in wide settings by attacking the semantic similarity of images. We present a new perturbation ...
在本文中，我们试图通过探索频域内对抗扰动的特征来揭示这种关系。我们采用小波包分解对对抗样本进行详细的频率分析，并在不同频段进行统计检验。有趣的是，我们的发现表明，在 ...
In this paper, we creatively leverage the frequency information to promote the imperceptibility and adversarial transferability in the white-box scenario and ...
Missing: Perspective | Show results with:Perspective
Oct 21, 2024 · We propose a novel approach termed Frequency-Guided Sample Relevance Attack (FGSRA). Specifically, we leverage frequency information to explore similar ...
Missing: Driven | Show results with:Driven