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This paper proposes an adaptively stochastic smoothing based approach for adversarial robustness certification of malware detection models, which it can at ...
May 1, 2024 · In this paper, we introduce a certifiable defense against patch attacks that guarantees, for a given executable and an adversarial patch size, no adversarial ...
Missing: ASSC: Adaptively Stochastic Models.
ASSC: Adaptively Stochastic Smoothing based Adversarial Robustness Certification of Malware Detection Models. ... Can LLMs Deeply Detect Complex Malicious ...
ASSC: Adaptively Stochastic Smoothing based Adversarial Robustness Certification of Malware Detection Models. Conference Paper. May 2024. Chun Yang · Gang Shi ...
In this paper, we propose a new method that generates adversarial examples based on affine-shear transformation from the perspective of deep model input layers.
ABSTRACT. Certified defenses are a recent development in adversarial machine learning (ML), which aim to rigorously guarantee the robustness of. ML models ...
Missing: ASSC: Adaptively
Nov 13, 2021 · In this paper, we provide TSS-a unified framework for certifying ML robustness against general adversarial semantic transformations.
Missing: ASSC: Adaptively Stochastic
Aug 1, 2024 · Robustness certification aims to measure the risk of adversarial examples, while randomized smoothing provides both certification and mitigation ...
ASSC: Adaptively Stochastic Smoothing based Adversarial Robustness Certification of Malware Detection Models. Conference Paper. May 2024. Chun Yang · Gang Shi ...
We integrate these novel techniques into our TSS framework and further propose a progressive-sampling-based strategy to accelerate the robustness certification.