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Author

Hailong Jiang

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Aug 2026

Robustness evaluation of explainable AI methods under natural image corruptions

This study presents the first comprehensive evaluation framework systematically assessing XAI robustness under natural image corruptions encountered in production environments and establishes the first evidence-based XAI robustness ranking under natural corruptions, providing actionable guidance for practitioners selecting methods in real-world applications where input quality cannot be guaranteed.

Guilin Zhang, Wulan Guo, Ziqi Tan et al. · 0 citations