Publications

Edoardo Debenedetti, Nicholas Carlini, Florian Tramèr (2023). Evading Black-box Classifiers Without Breaking Eggs. arXiv.

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Edoardo Debenedetti, Vikash Sehwag, Prateek Mittal (2022). A Light Recipe to Train Robust Vision Transformers. IEEE SaTML 2023.

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Edoardo Debenedetti (2022). Adversarially Robust Vision Transformers. EPFL.

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Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein (2021). RobustBench: A standardized benchmark for adversarial robustness. NeurIPS 2021 Datasets and Benchmarks Track.

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Edoardo Debenedetti, Dan Shaked Renous, Faezeh Nassajian (2020). [Re] Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks. NeurIPS 2019 Reproducibility Challenge Report Submissions.

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