Randomized Smoothing

Randomized smoothing is a technique for enhancing the robustness of machine learning models, particularly deep neural networks, against adversarial attacks—small, carefully crafted input perturbations designed to mislead the model. Current research focuses on improving the efficiency and effectiveness of smoothing methods, exploring various noise distributions and model architectures (including vision transformers and diffusion models), and extending its application to diverse data types like time series and medical images. This work is significant because it provides provable guarantees of robustness, a crucial step towards deploying reliable machine learning systems in safety-critical applications, and is actively advancing the theoretical understanding of model robustness and certification.

Papers

February 13, 2024

Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing
Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz
Segmentation Based Approach Adversarial Sample Randomized Smoothing Fine Grained Class Novel Certification Certification Algorithm

February 12, 2024

Accelerated Smoothing: A Scalable Approach to Randomized Smoothing
Devansh Bhardwaj, Kshitiz Kaushik, Sarthak Gupta
Adversarial Attack Randomized Smoothing Scalable Approach Smoothed Classifier Robustness Certificate Smoothing Algorithm Surrogate Neural Network

December 19, 2023

Rethinking Randomized Smoothing from the Perspective of Scalability
Anupriya Kumari, Devansh Bhardwaj, Sukrit Jindal
Timely Survey Adversarial Attack Native Robustness Adversarial Perturbation Appropriate Trust Randomized Smoothing Certified Defense

December 14, 2023

Exploring Transferability for Randomized Smoothing
Kai Qiu, Huishuai Zhang, Zhirong Wu, Stephen Lin
Native Robustness Foundation Model Task Transferability Model Robustness Robust Model Randomized Smoothing Semantic Learning

November 26, 2023

Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs
Zheyuan Hu, Zhouhao Yang, Yezhen Wang, George Em Karniadakis, Kenji Kawaguchi
Physic Informed Neural Network High Dimensional Randomized Smoothing High Dimensional Partial Differential Equation Bias Variance Physic Informed Loss

November 15, 2023

Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing
A K Nirala, A Joshi, C Hegde, S Sarkar
Vision Language Model Vision Language Open Vocabulary Randomized Smoothing Incremental Task Zero Shot Open Vocabulary

October 25, 2023

Multi-scale Diffusion Denoised Smoothing
Jongheon Jeong, Jinwoo Shin
Multi Scale Denoising Diffusion Randomized Smoothing Smoothed Classifier Implicit Denoising

October 24, 2023

Hierarchical Randomized Smoothing
Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
Native Robustness Randomized Smoothing Robustness Guarantee

October 12, 2023

Provably Robust Cost-Sensitive Learning via Randomized Smoothing
Yuan Xin, Michael Backes, Xiao Zhang
Adversarial Perturbation Robust Learning Randomized Smoothing Robustness Certification Cost Sensitive

October 11, 2023

Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches
Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng
Native Robustness Adversarial Robustness Randomized Smoothing Smoothed Classifier Cost Effective Approach

October 5, 2023

Certification of Deep Learning Models for Medical Image Segmentation
Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon, Nikos Paragios, Marie-Pierre Revel, Maria Vakalopoulou
Deep Learning Model Medical Image Segmentation Segmentation Model Randomized Smoothing Smoothing Factor Segmentation Baseline

September 28, 2023

The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
Blaise Delattre, Alexandre Araujo, Quentin Barthélemy, Alexandre Allauzen
Native Robustness Randomized Smoothing Robust Classifier Smoothed Classifier Lipschitz Loss Robustness Radius

September 25, 2023

Projected Randomized Smoothing for Certified Adversarial Robustness
Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi
Randomized Smoothing Robust Classifier Dimensional Approximation Smoothed Classifier

August 17, 2023

June 27, 2023

[Re] Double Sampling Randomized Smoothing
Aryan Gupta, Sarthak Gupta, Abhay Kumar, Harsh Dugar
Native Robustness Adversarial Training Adversarial Perturbation Randomized Smoothing Robustness Certification

June 16, 2023

Towards Better Certified Segmentation via Diffusion Models
Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon, Marie-Pierre Revel, Siddharth Garg, Farshad Khorrami, Maria Vakalopoulou
Diffusion Model Segmentation Model Image Segmentation Randomized Smoothing Segmentation Prediction

May 8, 2023

Understanding Noise-Augmented Training for Randomized Smoothing
Ambar Pal, Jeremias Sulam
Adversarial Attack Randomized Smoothing Smoothed Classifier Input Distribution

May 5, 2023

Random Smoothing Regularization in Kernel Gradient Descent Learning
Liang Ding, Tianyang Hu, Jiahang Jiang, Donghao Li, Wenjia Wang, Yuan Yao
Regularization Model Randomized Smoothing Kernel Based Random Data Augmentation

April 20, 2023

Certified Adversarial Robustness Within Multiple Perturbation Bounds
Soumalya Nandi, Sravanti Addepalli, Harsh Rangwani, R. Venkatesh Babu
Adversarial Attack Native Robustness Adversarial Robustness Randomized Smoothing Perturbation Bound