mlsanityData to 7 Oct 2026

TA3: Testing Against Adversarial Attacks on Machine Learning Models

arXiv 2410.05334 ↗First posted 6 Oct 2024cs.CR cs.LGcited by 0

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Abstract (arXiv)

Adversarial attacks are major threats to the deployment of machine learning (ML) models in many applications. Testing ML models against such attacks is becoming an essential step for evaluating and improving ML models. In this paper, we report the design and development of an interactive system for aiding the workflow of Testing Against Adversarial Attacks (TA3). In particular, with TA3, human-in-the-loop (HITL) enables human-steered attack simulation and visualization-assisted attack impact evaluation. While the current version of TA3 focuses on testing decision tree models against adversarial attacks based on the One Pixel Attack Method, it demonstrates the importance of HITL in ML testing and the potential application of HITL to the ML testing workflows for other types of ML models and other types of adversarial attacks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 92 references that are in the map. First posted on arXiv 6 Oct 2024.

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35 of 92 references.

References · 35

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23 more
  1. 2018Dataset Distillationcited by 224
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  17. 2022Explainable Global Fairness Verification of Tree-Based Classifierscited by 5
  18. 2023Segment (Almost) Nothing: Prompt-Agnostic Adversarial Attacks on Segmentation Modelscited by 5
  19. 2021Visualizing Ensemble Predictions of Music Moodcited by 4
  20. 2017How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Gamescited by 3
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  23. 2023Radial Icicle Tree (RIT): Node Separation and Area Constancycited by 1

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