How many perturbations break this model? Evaluating robustness beyond
adversarial accuracy

How many perturbations break this model? Evaluating robustness beyond adversarial accuracy

8 July 2022

Bhiksha Raj

Papers citing "How many perturbations break this model? Evaluating robustness beyond adversarial accuracy"

6 / 6 papers shown

Title
The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance? Sourav Banerjee Ayushi Agarwal Eishkaran Singh ELM 73 2 0 02 Dec 2024
An Analytic Solution to Covariance Propagation in Neural Networks Oren Wright Yorie Nakahira José M. F. Moura 21 5 0 24 Mar 2024
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume Ping Guo Cheng Gong Xi Lin Zhiyuan Yang Qingfu Zhang AAML 31 2 0 08 Mar 2024
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models Zaitang Li Pin-Yu Chen Tsung-Yi Ho AAML DiffM 32 4 0 19 Apr 2023
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Hanxun Huang Yisen Wang S. Erfani Quanquan Gu James Bailey Xingjun Ma AAML TPM 46 100 0 07 Oct 2021
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 234 678 0 19 Oct 2020