Rewriting the Budget: A General Framework for Black-Box Attacks Under Cost Asymmetry
Mahdi Salmani, Alireza Abdollahpoorrostam, Seyed-Mohsen Moosavi-Dezfooli

TL;DR
This paper introduces a flexible framework for black-box adversarial attacks that accounts for asymmetric query costs, optimizing attack strategies to minimize total cost while maintaining effectiveness.
Contribution
It proposes novel asymmetric search and gradient estimation methods, enabling existing attacks to reduce total query costs under asymmetric cost conditions.
Findings
Achieves up to 40% reduction in total attack cost.
Maintains attack effectiveness with smaller perturbations.
Applicable to various black-box attack algorithms.
Abstract
Traditional decision-based black-box adversarial attacks on image classifiers aim to generate adversarial examples by slightly modifying input images while keeping the number of queries low, where each query involves sending an input to the model and observing its output. Most existing methods assume that all queries have equal cost. However, in practice, queries may incur asymmetric costs; for example, in content moderation systems, certain output classes may trigger additional review, enforcement, or penalties, making them more costly than others. While prior work has considered such asymmetric cost settings, effective algorithms for this scenario remain underdeveloped. In this paper, we propose a general framework for decision-based attacks under asymmetric query costs, which we refer to as asymmetric black-box attacks. We modify two core components of existing attacks: the search…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdversarial Robustness in Machine Learning · Advanced Graph Neural Networks · Domain Adaptation and Few-Shot Learning
MethodsFocus
