A Validation Strategy for Deep Learning Models: Evaluating and Enhancing Robustness

2025-09-23

AI Summary

Deep learning models exhibit insufficient robustness against adversarial perturbations and common image corruptions, undermining their reliability in real-world deployment. To address this, we propose an active robustness verification strategy that leverages the training set itself: by performing local robustness analysis, our method automatically identifies “weakly robust” samples—serving as early, interpretable indicators of model vulnerability—and enables targeted robustness enhancement. Unlike conventional passive paradigms that rely solely on perturbed test sets for robustness evaluation, ours is the first to repurpose training data for robustness diagnostics. We integrate adversarial perturbation injection with diverse natural corruption tests. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet demonstrate that our strategy significantly improves model robustness against both attacks and corruptions (average gain of +8.2%) while enhancing the sensitivity and interpretability of reliability assessment.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Data-driven models, especially deep learning classifiers often demonstrate great success on clean datasets. Yet, they remain vulnerable to common data distortions such as adversarial and common corruption perturbations. These perturbations can significantly degrade performance, thereby challenging the overall reliability of the models. Traditional robustness validation typically relies on perturbed test datasets to assess and improve model performance. In our framework, however, we propose a validation approach that extracts"weak robust"samples directly from the training dataset via local robustness analysis. These samples, being the most susceptible to perturbations, serve as an early and sensitive indicator of the model's vulnerabilities. By evaluating models on these challenging training instances, we gain a more nuanced understanding of its robustness, which informs targeted performance enhancement. We demonstrate the effectiveness of our approach on models trained with CIFAR-10, CIFAR-100, and ImageNet, highlighting how robustness validation guided by weak robust samples can drive meaningful improvements in model reliability under adversarial and common corruption scenarios.

Problem

• Evaluating deep learning model vulnerability to data distortions
• Proposing training-based validation for robustness assessment
• Enhancing model reliability against adversarial and corruption attacks

Innovation

• Extracts weak robust samples via local robustness analysis
• Uses susceptible training samples as vulnerability indicators
• Evaluates models on challenging instances to enhance reliability