Get Global Guarantees: On the Probabilistic Nature of Perturbation Robustness

• URL: http://arxiv.org/abs/2508.19183v1
• Date: Tue, 26 Aug 2025 16:41:04 GMT
• Title: Get Global Guarantees: On the Probabilistic Nature of Perturbation Robustness
• Authors: Wenchuan Mu, Kwan Hui Lim,
• Abstract summary: In safety-critical deep learning applications, robustness measures the ability of neural models that handle imperceptible perturbations in input data.<n>Existing pre-deployment robustness assessment methods typically suffer from significant trade-offs between computational cost and measurement precision.<n>We propose tower robustness to evaluate robustness, which is a novel, practical metric based on hypothesis testing.
• Score: 10.738378139028976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In safety-critical deep learning applications, robustness measures the ability of neural models that handle imperceptible perturbations in input data, which may lead to potential safety hazards. Existing pre-deployment robustness assessment methods typically suffer from significant trade-offs between computational cost and measurement precision, limiting their practical utility. To address these limitations, this paper conducts a comprehensive comparative analysis of existing robustness definitions and associated assessment methodologies. We propose tower robustness to evaluate robustness, which is a novel, practical metric based on hypothesis testing to quantitatively evaluate probabilistic robustness, enabling more rigorous and efficient pre-deployment assessments. Our extensive comparative evaluation illustrates the advantages and applicability of our proposed approach, thereby advancing the systematic understanding and enhancement of model robustness in safety-critical deep learning applications.

Related papers

• Advancing Embodied Agent Security: From Safety Benchmarks to Input Moderation [52.83870601473094]
  Embodied agents exhibit immense potential across a multitude of domains.<n>Existing research predominantly concentrates on the security of general large language models.<n>This paper introduces a novel input moderation framework, meticulously designed to safeguard embodied agents.
  arXiv  Detail & Related papers  (2025-04-22T08:34:35Z)
• Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guide [2.152298082788376]
  Probable robustness (PR) offers a more practical perspective by quantifying the likelihood of failures under perturbations.<n>This paper provides a concise yet comprehensive overview of PR, covering its formal definitions, evaluation and enhancement methods.<n>We explore the integration of PR verification evidence into system-level safety assurance, addressing challenges in translating DL model-level robustness to system-level claims.
  arXiv  Detail & Related papers  (2025-02-20T18:47:17Z)
• A practical approach to evaluating the adversarial distance for machine learning classifiers [2.2120851074630177]
  This paper investigates the estimation of the more informative adversarial distance using iterative adversarial attacks and a certification approach. We find that our adversarial attack approach is effective compared to related implementations, while the certification method falls short of expectations.
  arXiv  Detail & Related papers  (2024-09-05T14:57:01Z)
• Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations [80.86128012438834]
  We show for the first time that computing the robustness of counterfactuals with respect to plausible model shifts is NP-complete. We propose a novel probabilistic approach which is able to provide tight estimates of robustness with strong guarantees.
  arXiv  Detail & Related papers  (2024-07-10T09:13:11Z)
• Towards Precise Observations of Neural Model Robustness in Classification [2.127049691404299]
  In deep learning applications, robustness measures the ability of neural models that handle slight changes in input data. Our approach contributes to a deeper understanding of model robustness in safety-critical applications.
  arXiv  Detail & Related papers  (2024-04-25T09:37:44Z)
• Boosting Adversarial Robustness using Feature Level Stochastic Smoothing [46.86097477465267]
  adversarial defenses have led to a significant improvement in the robustness of Deep Neural Networks. In this work, we propose a generic method for introducingity in the network predictions. We also utilize this for smoothing decision rejecting low confidence predictions.
  arXiv  Detail & Related papers  (2023-06-10T15:11:24Z)
• Differential privacy and robust statistics in high dimensions [49.50869296871643]
  High-dimensional Propose-Test-Release (HPTR) builds upon three crucial components: the exponential mechanism, robust statistics, and the Propose-Test-Release mechanism. We show that HPTR nearly achieves the optimal sample complexity under several scenarios studied in the literature.
  arXiv  Detail & Related papers  (2021-11-12T06:36:40Z)
• Probabilistic robust linear quadratic regulators with Gaussian processes [73.0364959221845]
  Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. We present a novel controller synthesis for linearized GP dynamics that yields robust controllers with respect to a probabilistic stability margin.
  arXiv  Detail & Related papers  (2021-05-17T08:36:18Z)
• Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification [81.32981236437395]
  We present a semi-formal verification approach for decision-making tasks based on interval analysis. Our method obtains comparable results over standard benchmarks with respect to formal verifiers. Our approach allows to efficiently evaluate safety properties for decision-making models in practical applications.
  arXiv  Detail & Related papers  (2020-10-19T11:18:06Z)
• SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
  SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
  arXiv  Detail & Related papers  (2020-06-12T10:40:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.