Quantifying Adversarial Uncertainty in Evidential Deep Learning using Conflict Resolution

• URL: http://arxiv.org/abs/2506.05937v1
• Date: Fri, 06 Jun 2025 10:06:23 GMT
• Title: Quantifying Adversarial Uncertainty in Evidential Deep Learning using Conflict Resolution
• Authors: Charmaine Barker, Daniel Bethell, Simos Gerasimou,
• Abstract summary: Conflict-aware Evidential Deep Learning (C-EDL) is a lightweight post-hoc uncertainty quantification approach.<n>C-EDL generates diverse, task-preserving transformations per input and quantifies disagreement to calibrate uncertainty estimates.<n>Our experimental evaluation shows that C-EDL significantly outperforms state-of-the-art EDL variants and competitive baselines.
• Score: 2.321323878201932
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reliability of deep learning models is critical for deployment in high-stakes applications, where out-of-distribution or adversarial inputs may lead to detrimental outcomes. Evidential Deep Learning, an efficient paradigm for uncertainty quantification, models predictions as Dirichlet distributions of a single forward pass. However, EDL is particularly vulnerable to adversarially perturbed inputs, making overconfident errors. Conflict-aware Evidential Deep Learning (C-EDL) is a lightweight post-hoc uncertainty quantification approach that mitigates these issues, enhancing adversarial and OOD robustness without retraining. C-EDL generates diverse, task-preserving transformations per input and quantifies representational disagreement to calibrate uncertainty estimates when needed. C-EDL's conflict-aware prediction adjustment improves detection of OOD and adversarial inputs, maintaining high in-distribution accuracy and low computational overhead. Our experimental evaluation shows that C-EDL significantly outperforms state-of-the-art EDL variants and competitive baselines, achieving substantial reductions in coverage for OOD data (up to 55%) and adversarial data (up to 90%), across a range of datasets, attack types, and uncertainty metrics.

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