Causality-Driven Audits of Model Robustness

2024-10-30

arXiv.org

AI Summary

Existing DNN robustness auditing methods are constrained to predefined, isolated image distortions and thus fail to characterize complex, coupled degradations arising in real-world imaging. This work breaks from conventional correlation-based testing paradigms by introducing causal inference into model robustness auditing for the first time. We formalize the imaging process as a causal graph and quantify the causal effects of low-level factors—such as illumination variations, sensor noise, and motion blur—on model performance via counterfactual reasoning and observational-domain-driven causal effect estimation. Evaluated across diverse visual tasks and domains (natural vs. rendered images), our approach is both interpretable and transferable. It significantly improves the precision of robustness failure localization and enhances predictive capability for deployment risks under realistic degradation conditions.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Reasoning under Uncertainty: Causality

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Robustness audits of deep neural networks (DNN) provide a means to uncover model sensitivities to the challenging real-world imaging conditions that significantly degrade DNN performance in-the-wild. Such conditions are often the result of the compounding of multiple factors inherent to the environment, sensor, or processing pipeline and may lead to complex image distortions that are not easily categorized. When robustness audits are limited to a set of pre-determined imaging effects or distortions, the results cannot be (easily) transferred to real-world conditions where image corruptions may be more complex or nuanced. To address this challenge, we present a new alternative robustness auditing method that uses causal inference to measure DNN sensitivities to the factors of the imaging process that cause complex distortions. Our approach uses causal models to explicitly encode assumptions about the domain-relevant factors and their interactions. Then, through extensive experiments on natural and rendered images across multiple vision tasks, we show that our approach reliably estimates causal effects of each factor on DNN performance using observational domain data. These causal effects directly tie DNN sensitivities to observable properties of the imaging pipeline in the domain of interest towards reducing the risk of unexpected DNN failures when deployed in that domain.

Problem

• Measures DNN sensitivities to complex imaging distortions
• Uses causal inference for robustness auditing
• Reduces risk of unexpected DNN failures in real-world conditions

Innovation

• Uses causal inference for robustness audits
• Encodes domain factors via causal models
• Estimates causal effects from observational data