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cs.CR2026
Attacks Meet Interpretability (AmI) Evaluation and Findings
Qian Ma, Ziping Ye, Shagufta Mehnaz
To investigate the effectiveness of the model explanation in detecting adversarial examples, we reproduce the results of two papers, Attacks Meet Interpretability: Attribute-steere…
cs.CR2025
Enhancing Adversarial Example Detection Through Model Explanation
Qian Ma, Ziping Ye
Adversarial examples are a major problem for machine learning models, leading to a continuous search for effective defenses. One promising direction is to leverage model explanatio…
cs.CR2024
Leveraging MTD to Mitigate Poisoning Attacks in Decentralized FL with Non-IID Data
Chao Feng, Alberto Huertas Celdrán, Zien Zeng +4
Decentralized Federated Learning (DFL), a paradigm for managing big data in a privacy-preserved manner, is still vulnerable to poisoning attacks where malicious clients tamper with…