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Exploring Transferability for Randomized Smoothing

Exploring Transferability for Randomized Smoothing

14 December 2023
Kai Qiu
Huishuai Zhang
Zhirong Wu
Stephen Lin
    AAML
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Papers citing "Exploring Transferability for Randomized Smoothing"

11 / 11 papers shown
Title
SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks
SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks
Alexander Robey
Eric Wong
Hamed Hassani
George J. Pappas
AAML
103
247
0
05 Oct 2023
Universal and Transferable Adversarial Attacks on Aligned Language
  Models
Universal and Transferable Adversarial Attacks on Aligned Language Models
Andy Zou
Zifan Wang
Nicholas Carlini
Milad Nasr
J. Zico Kolter
Matt Fredrikson
282
1,436
0
27 Jul 2023
Diffusion Models for Adversarial Purification
Diffusion Models for Adversarial Purification
Weili Nie
Brandon Guo
Yujia Huang
Chaowei Xiao
Arash Vahdat
Anima Anandkumar
WIGM
250
439
0
16 May 2022
Flamingo: a Visual Language Model for Few-Shot Learning
Flamingo: a Visual Language Model for Few-Shot Learning
Jean-Baptiste Alayrac
Jeff Donahue
Pauline Luc
Antoine Miech
Iain Barr
...
Mikolaj Binkowski
Ricardo Barreira
Oriol Vinyals
Andrew Zisserman
Karen Simonyan
MLLM
VLM
344
3,515
0
29 Apr 2022
Masked Autoencoders Are Scalable Vision Learners
Masked Autoencoders Are Scalable Vision Learners
Kaiming He
Xinlei Chen
Saining Xie
Yanghao Li
Piotr Dollár
Ross B. Girshick
ViT
TPM
427
7,705
0
11 Nov 2021
MACER: Attack-free and Scalable Robust Training via Maximizing Certified
  Radius
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius
Runtian Zhai
Chen Dan
Di He
Huan Zhang
Boqing Gong
Pradeep Ravikumar
Cho-Jui Hsieh
Liwei Wang
OOD
AAML
85
176
0
08 Jan 2020
Certified Adversarial Robustness via Randomized Smoothing
Certified Adversarial Robustness via Randomized Smoothing
Jeremy M. Cohen
Elan Rosenfeld
J. Zico Kolter
AAML
130
2,028
0
08 Feb 2019
Obfuscated Gradients Give a False Sense of Security: Circumventing
  Defenses to Adversarial Examples
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Anish Athalye
Nicholas Carlini
D. Wagner
AAML
193
3,180
0
01 Feb 2018
Evasion Attacks against Machine Learning at Test Time
Evasion Attacks against Machine Learning at Test Time
Battista Biggio
Igino Corona
Davide Maiorca
B. Nelson
Nedim Srndic
Pavel Laskov
Giorgio Giacinto
Fabio Roli
AAML
141
2,147
0
21 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILM
OOD
275
12,029
0
19 Jun 2017
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
245
14,893
1
21 Dec 2013
1