Introduces Black-CL black-box benchmark and BETA textual-prototype method that matches or exceeds white-box continual learning performance on ten datasets using 0.05M parameters.
Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available. We introduce a framework that conceptually unifies much of the existing work on black-box attacks, and we demonstrate that the current state-of-the-art methods are optimal in a natural sense. Despite this optimality, we show how to improve black-box attacks by bringing a new element into the problem: gradient priors. We give a bandit optimization-based algorithm that allows us to seamlessly integrate any such priors, and we explicitly identify and incorporate two examples. The resulting methods use two to four times fewer queries and fail two to five times less often than the current state-of-the-art.
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Introduces a budget-adaptive black-box adversarial patch attack that jointly optimizes location, size, and appearance for object detectors with plain-view success metrics.
citing papers explorer
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Black-Box Continual Learning for Vision-Language Models
Introduces Black-CL black-box benchmark and BETA textual-prototype method that matches or exceeds white-box continual learning performance on ten datasets using 0.05M parameters.
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Budget-Aware Adaptive Adversarial Patches for Black-Box Object Detection
Introduces a budget-adaptive black-box adversarial patch attack that jointly optimizes location, size, and appearance for object detectors with plain-view success metrics.