Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks
Machine learning models are increasingly adapted in various domains. However, adversarial examples pose a significant threat to the reliable deployment of these models. In recent years, some powerful adversarial example attacks have been proposed for the fast and query-efficient generation of adversarial examples, even in black-box scenarios, highlighting the need for scalable, low-cost, and powerful defenses. In this work, we present two contributions to the domain of black-box adversarial example attacks and defenses. First, we propose Random Logit Scaling (RLS), a randomization-based defens
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- PossiblePossibly related (embedding) · 46%Something keeps turning up in my prompt injection detection logs that I didn't expect. Curious if others doing LLM security work have seen it. [D] →
- LinkedLinked via arxiv author · 85%Hamid Dashtbani →
“Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks”
- LinkedLinked via arxiv author · 85%Mehdi Dousti Gandomani →
“Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks”
- LinkedLinked via arxiv author · 85%AmirMahdi Sadeghzadeh →
“Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks”