Randomized Smoothing of All Shapes and Sizes

19 February 2020

Papers citing "Randomized Smoothing of All Shapes and Sizes"

50 / 52 papers shown

Title
CeTAD: Towards Certified Toxicity-Aware Distance in Vision Language Models Xiangyu Yin Jiaxu Liu Zhen Chen Jinwei Hu Yi Dong Xiaowei Huang Wenjie Ruan AAML 50 0 0 08 Mar 2025
ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs Yuchen Yang Shubham Ugare Yifan Zhao Gagandeep Singh Sasa Misailovic MQ 31 0 0 31 Oct 2024
Treatment of Statistical Estimation Problems in Randomized Smoothing for Adversarial Robustness Vaclav Voracek AAML 46 1 0 25 Jun 2024
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness Jieren Deng Hanbin Hong A. Palmer Xin Zhou Jinbo Bi Kaleel Mahmood Yuan Hong Derek Aguiar AAML 40 0 0 25 May 2024
Boosting Few-Pixel Robustness Verification via Covering Verification Designs Yuval Shapira Naor Wiesel Shahar Shabelman Dana Drachsler-Cohen AAML 34 0 0 17 May 2024
Accelerated Smoothing: A Scalable Approach to Randomized Smoothing Devansh Bhardwaj Kshitiz Kaushik Sarthak Gupta AAML 37 0 0 12 Feb 2024
Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing Ashutosh Nirala Ameya Joshi Chinmay Hegde S Sarkar VLM 36 0 0 15 Nov 2023
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness Ambar Pal Huaijin Hao Rene Vidal 26 8 0 28 Sep 2023
Incremental Randomized Smoothing Certification Shubham Ugare Tarun Suresh Debangshu Banerjee Gagandeep Singh Sasa Misailovic AAML 38 8 0 31 May 2023
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection Nicola Franco Daniel Korth J. Lorenz Karsten Roscher Stephan Guennemann 33 5 0 27 Mar 2023
A Unified Algebraic Perspective on Lipschitz Neural Networks Alexandre Araujo Aaron J. Havens Blaise Delattre A. Allauzen Bin Hu AAML 36 52 0 06 Mar 2023
On the Robustness of Randomized Ensembles to Adversarial Perturbations Hassan Dbouk Naresh R Shanbhag AAML 23 7 0 02 Feb 2023
Towards Large Certified Radius in Randomized Smoothing using Quasiconcave Optimization Bo-Han Kung Shang-Tse Chen AAML 24 0 0 01 Feb 2023
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion Zhuoqun Huang Neil G. Marchant Keane Lucas Lujo Bauer O. Ohrimenko Benjamin I. P. Rubinstein AAML 32 15 0 31 Jan 2023
Confidence-aware Training of Smoothed Classifiers for Certified Robustness Jongheon Jeong Seojin Kim Jinwoo Shin AAML 21 7 0 18 Dec 2022
Private and Reliable Neural Network Inference Nikola Jovanović Marc Fischer Samuel Steffen Martin Vechev 22 14 0 27 Oct 2022
Accelerating Certified Robustness Training via Knowledge Transfer Pratik Vaishnavi Kevin Eykholt Amir Rahmati 24 7 0 25 Oct 2022
Adversarial Pretraining of Self-Supervised Deep Networks: Past, Present and Future Guo-Jun Qi M. Shah SSL 23 8 0 23 Oct 2022
Certified Training: Small Boxes are All You Need Mark Niklas Muller Franziska Eckert Marc Fischer Martin Vechev AAML 39 45 0 10 Oct 2022
CARE: Certifiably Robust Learning with Reasoning via Variational Inference Jiawei Zhang Linyi Li Ce Zhang Bo-wen Li AAML OOD 43 8 0 12 Sep 2022
RUSH: Robust Contrastive Learning via Randomized Smoothing Yijiang Pang Boyang Liu Jiayu Zhou OOD AAML 19 1 0 11 Jul 2022
Riemannian data-dependent randomized smoothing for neural networks certification Pol Labarbarie H. Hajri M. Arnaudon 29 4 0 21 Jun 2022
Double Sampling Randomized Smoothing Linyi Li Jiawei Zhang Tao Xie Bo-wen Li AAML 17 23 0 16 Jun 2022
Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis Raphael Ettedgui Alexandre Araujo Rafael Pinot Y. Chevaleyre Jamal Atif AAML 34 3 0 03 Jun 2022
(De-)Randomized Smoothing for Decision Stump Ensembles Miklós Z. Horváth Mark Niklas Muller Marc Fischer Martin Vechev 30 3 0 27 May 2022
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness Ameya Joshi Minh Pham Minsu Cho Leonid Boytsov Filipe Condessa J. Zico Kolter C. Hegde UQCV AAML 32 2 0 12 May 2022
A Simple Approach to Adversarial Robustness in Few-shot Image Classification Akshayvarun Subramanya Hamed Pirsiavash VLM 26 6 0 11 Apr 2022
Adversarially Robust Learning with Tolerance H. Ashtiani Vinayak Pathak Ruth Urner AAML 26 9 0 02 Mar 2022
Adversarial robustness of sparse local Lipschitz predictors Ramchandran Muthukumar Jeremias Sulam AAML 32 13 0 26 Feb 2022
Smoothed Embeddings for Certified Few-Shot Learning Mikhail Aleksandrovich Pautov Olesya Kuznetsova Nurislam Tursynbek Aleksandr Petiushko Ivan Oseledets 42 5 0 02 Feb 2022
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines Jiachen Sun Akshay Mehra B. Kailkhura Pin-Yu Chen Dan Hendrycks Jihun Hamm Z. Morley Mao AAML 38 21 0 01 Dec 2021
On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples Adelin Travers Lorna Licollari Guanghan Wang Varun Chandrasekaran Adam Dziedzic David Lie Nicolas Papernot AAML 28 3 0 03 Aug 2021
On the Certified Robustness for Ensemble Models and Beyond Zhuolin Yang Linyi Li Xiaojun Xu B. Kailkhura Tao Xie Bo-wen Li AAML 29 48 0 22 Jul 2021
Scalable Certified Segmentation via Randomized Smoothing Marc Fischer Maximilian Baader Martin Vechev 18 38 0 01 Jul 2021
Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks Yulong Cao* Ningfei Wang* Chaowei Xiao Dawei Yang Jin Fang Ruigang Yang Qi Alfred Chen Mingyan D. Liu Bo-wen Li AAML 24 218 0 17 Jun 2021
Relating Adversarially Robust Generalization to Flat Minima David Stutz Matthias Hein Bernt Schiele OOD 36 65 0 09 Apr 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 35 25 0 20 Mar 2021
Adversarial Training is Not Ready for Robot Learning Mathias Lechner Ramin Hasani Radu Grosu Daniela Rus T. Henzinger AAML 38 34 0 15 Mar 2021
On the Paradox of Certified Training Nikola Jovanović Mislav Balunović Maximilian Baader Martin Vechev OOD 28 13 0 12 Feb 2021
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression Masanori Yamada Sekitoshi Kanai Tomoharu Iwata Tomokatsu Takahashi Yuki Yamanaka Hiroshi Takahashi Atsutoshi Kumagai AAML 16 9 0 05 Feb 2021
Data-Dependent Randomized Smoothing Motasem Alfarra Adel Bibi Philip Torr Guohao Li UQCV 28 34 0 08 Dec 2020
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations Jinyuan Jia Binghui Wang Xiaoyu Cao Hongbin Liu Neil Zhenqiang Gong 16 24 0 15 Nov 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 29 48 0 19 Oct 2020
Higher-Order Certification for Randomized Smoothing Jeet Mohapatra Ching-Yun Ko Tsui-Wei Weng Pin-Yu Chen Sijia Liu Luca Daniel AAML 20 44 0 13 Oct 2020
Adversarial Boot Camp: label free certified robustness in one epoch Ryan Campbell Chris Finlay Adam M. Oberman AAML 25 0 0 05 Oct 2020
Certifying Confidence via Randomized Smoothing Aounon Kumar Alexander Levine S. Feizi Tom Goldstein UQCV 33 39 0 17 Sep 2020
SoK: Certified Robustness for Deep Neural Networks Linyi Li Tao Xie Bo-wen Li AAML 33 128 0 09 Sep 2020
Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman Andrew Ilyas Logan Engstrom Ashish Kapoor A. Madry 37 417 0 16 Jul 2020
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework Dinghuai Zhang Mao Ye Chengyue Gong Zhanxing Zhu Qiang Liu AAML 24 62 0 21 Feb 2020
$Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images$ Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images Avrim Blum Travis Dick N. Manoj Hongyang R. Zhang AAML 31 79 0 10 Feb 2020