biostudies-literatureUnknown16(1)Tuo YToday'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 eachScientific reports6886https://www.ebi.ac.uk/biostudies/studies/S-EPMC12916764biostudies-literatureQuery-efficient decision-based adversarial attack with low query budget.PMC12916764Tuo YChe SYin MfalseQuery-efficient decision-based adversarial attack with low query budget.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 each2026-01-01T00:00:00Z2026 Feb2026-07-16T09:23:06.092Z2026-07-09T10:46:40.032ZS-EPMC129167644162945210.1038/s41598-026-38428-4