Robustness testing systematically evaluates how well neural networks maintain performance under various forms of input perturbations, distribution shifts, and adversarial conditions.
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Adversarial Testing
Evaluate resistance to malicious input perturbations
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Distribution Shift Testing
Test performance under natural data variations
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Corruption Testing
Assess robustness to common image corruptions
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Stress Testing
Push models to their operational limits
Robustness Metrics
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Robust Accuracy
Accuracy under adversarial perturbations
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Certified Radius
Provable robustness guarantees
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Attack Success Rate
Percentage of successful adversarial attacks
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Prediction Consistency
Stability across input variations
Robustness Testing Dashboard
Model Performance Summary
94.2%
Clean Accuracy
67.8%
Robust Accuracy (Īµ=0.1)
32.2%
Attack Success Rate
0.047
Certified Radius
AutoAttack
Standardized adversarial benchmark suite with diverse attack methods
RobustBench
Comprehensive leaderboard for adversarial robustness evaluation
CleverHans
Python library for adversarial machine learning research
ImageNet-C
Benchmark for common corruption robustness testing