278 citations · 846 across the 26 of their papers we have counts for
4 papers · 1 filter
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag +5
As a research community, we are still lacking a systematic understanding of the progress on adversarial robustness which often makes it hard to identify the most promising ideas in…
Understanding and Improving Fast Adversarial Training
Maksym Andriushchenko, Nicolas Flammarion
A recent line of work focused on making adversarial training computationally efficient for deep learning models. In particular, Wong et al. (2020) showed that -adversa…
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
Francesco Croce, Maksym Andriushchenko, Naman D. Singh +2
We propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on subs…
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow
Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empiri…