Query-efficient decision-based adversarial attack with low query budget.

ABSTRACT: Today's deep neural networks have excellent performance on a variety of tasks. However, the emergence of adversarial examples has raised concerns about the security of the models. Attacks on real-world models are difficult because real-world models only return the final decision. While the decision-based attacks can generate adversarial examples solely based on the model's hard-label information, which makes it closest to real-world. However, existing decision-based attacks still require thousands of queries to generate good quality adversarial examples. Therefore, in order to generate high-quality adversarial examples with low query budget, we present a novel decision-based attack one plane one query attack (OPOQA). The main idea of the OPOQA is to generate more candidate examples in each

SUBMITTER: Tuo Y 

PROVIDER: S-EPMC12916764 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature