Hard-label Attack

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (9 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
  2 Feb 2024 1 repository listed
  Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible.
• LimeAttack: Local Explainable Method for Textual Hard-Label Adversarial Attack
  1 Aug 2023 1 repository listed
  Natural language processing models are vulnerable to adversarial examples.
• TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack
  20 Jan 2022 1 repository listed
  Existing textual adversarial attacks usually utilize the gradient or prediction confidence to generate adversarial examples, making it hard to be deployed in real-world applications.
• Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks
  15 Nov 2021 1 repository listed Syntology ran 2 of 2 samples · 0 unverified
  In this paper, we propose a novel geometric-based approach called Tangent Attack (TA), which identifies an optimal tangent point of a virtual hemisphere located on the decision boundary to reduce the distortion of the…
• RayS: A Ray Searching Method for Hard-label Adversarial Attack
  23 Jun 2020 1 repository listed
  Deep neural networks are vulnerable to adversarial attacks.
• Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
  24 Sep 2019 1 repository listed Syntology ran 0 of 4 samples · 4 unverified
  We study the most practical problem setup for evaluating adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where…

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.