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"Improving Adversarial Transferability via Frequency-based Stationary P" by Zhiyu Zhu, Huaming Chen et al.

Deep neural networks (DNNs) have been shown vulnerable to interference from adversarial samples, leading to erroneous predictions. Investigating adversarial attacks can effectively improve the reliability as well as the performance of deep neural models in real-world applications. Since it is generally challenging to infer the parameters in black-box models, high transferability becomes an important factor for the success rate of an attack method. Recently, the Spectrum Simulation Attack method exhibits promising results based on the frequency domain. In light of SSA, we propose a novel attack approach in this paper, which achieves the best results among diverse state-of-the-art transferable adversarial attack methods. Our method aims to find a stationary point, which extends the ability to find multiple local optima with the optimal local attack effect. After finding the stationary point, a frequency-based search is employed to explore the best adversarial samples in the neighbouring ....

Spectrum Simulation Attack , Adversarial Transferability , Black Box Attack , Requency Search , Stationary Point ,

Neue Gerätschaften der Ulrichsberger Florianis gesegnet

Neue Gerätschaften der Ulrichsberger Florianis gesegnet
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