研究人员开发了一种名为机会主义目标选择（OTS）的新方法，以提高黑盒对抗性攻击的效率。OTS充当一个包装器，允许攻击在早期阶段从无目标目标切换到有目标目标，从而减少查询浪费。该技术无需修改底层攻击模型或访问梯度，并在ImageNet分类器上进行了验证，显示出成功率和查询效率的显著提高。 AI
影响 提高了对抗性攻击的效率，可能影响AI模型鲁棒性测试和安全性。
排序理由 该集群包含一篇详细介绍对抗性攻击新研究方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.25663v1 Announce Type: new Abstract: Black-box adversarial attacks that minimize only the ground-truth confidence suffer from class drift: perturbations wander through the feature space without committing to a specific adversarial class, wasting queries on diffuse, und…
Black-box adversarial attacks that minimize only the ground-truth confidence suffer from class drift: perturbations wander through the feature space without committing to a specific adversarial class, wasting queries on diffuse, undirected progress. We introduce Opportunistic Tar…