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Found 3 papers, 2 papers with code
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack
We study the unique, less-well understood problem of generating sparse adversarial samples simply by observing the score-based replies to model queries.
Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models
The ability to extract information from solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world systems, such as autonomous cars or machine learning models exposed as a service (MLaaS).
RamBoAttack: A Robust Query Efficient Deep Neural Network Decision Exploit
In our study, we first deep dive into recent state-of-the-art decision-based attacks in ICLR and SP to highlight the costly nature of discovering low distortion adversarial employing gradient estimation methods.
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