Universal Adversarial Perturbations: A Survey

16 May 2020

Papers citing "Universal Adversarial Perturbations: A Survey"

9 / 9 papers shown

Title
Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Namhyuk Ahn Kiyoon Yoo Wonhyuk Ahn Daesik Kim Seung-Hun Nam AAML WIGM DiffM 85 0 0 16 Dec 2024
Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions Runhao Zeng Xiaoyong Chen Jiaming Liang Huisi Wu Guangzhong Cao Yong Guo AAML 32 3 0 29 Mar 2024
A Study on FGSM Adversarial Training for Neural Retrieval Simon Lupart S. Clinchant AAML 24 7 0 25 Jan 2023
On the Robustness of Explanations of Deep Neural Network Models: A Survey Amlan Jyoti Karthik Balaji Ganesh Manoj Gayala Nandita Lakshmi Tunuguntla Sandesh Kamath V. Balasubramanian XAI FAtt AAML 27 4 0 09 Nov 2022
Decorrelative Network Architecture for Robust Electrocardiogram Classification Christopher Wiedeman Ge Wang OOD 13 2 0 19 Jul 2022
Verifying Integrity of Deep Ensemble Models by Lossless Black-box Watermarking with Sensitive Samples Lina Lin Hanzhou Wu AAML 6 5 0 09 May 2022
Stealthy Attack on Algorithmic-Protected DNNs via Smart Bit Flipping B. Ghavami Seyd Movi Zhenman Fang Lesley Shannon AAML 19 9 0 25 Dec 2021
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 250 5,830 0 08 Jul 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,109 0 06 Jun 2015