arXiv:2202.05778
공식 코드 · Papers with Code
White-Box Attacks on Hate-speech BERT Classifiers in German with Explicit and Implicit Character Level Defense
원문 초록
In this work, we evaluate the adversarial robustness of BERT models trained on German Hate Speech datasets. We also complement our evaluation with two novel white-box character and word level attacks thereby contributing to the range of attacks available. Furthermore, we also perform a comparison of two novel character-level defense strategies and evaluate their robustness with one another.
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RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach
NIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness Analysis
The interplay of robustness and generalization in quantum machine learning
Towards Class-wise Fair Adversarial Training via Anti-Bias Soft Label Distillation
PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies
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