iclr23-bands.github.io - Overview | Backdoor Attacks and Defenses in Machine Learning (BANDS)

home > domains > iclr23-bands.github.io

Description: Workshop at ICLR 2023, Virtual

Example domain paragraphs

Backdoor attacks aim to cause consistent misclassification of any input by adding a specific pattern called a trigger. Unlike adversarial attacks requiring generating perturbations on the fly to induce misclassification for one single input, backdoor attacks have prompt effects by simply applying a pre-chosen trigger. Recent studies have shown the feasibility of launching backdoor attacks in various domains, such as computer vision (CV), natural language processing (NLP), federated learning (FL), etc. As ba

This workshop, B ackdoor A ttacks a N d D efen S es in Machine Learning ( BANDS ), aims to bring together researchers from government, academia, and industry that share a common interest in exploring and building more secure machine learning models against backdoor attacks.

Speakers are in alphabetical order by last name.

Links to iclr23-bands.github.io (5)

• kaiyuanzhang.com Kaiyuan Zhang @Purdue University
• yifeiwang77.com Yifei Wang
• trojan-removal.com IEEE TRC'22 – Trojan Removal Competition
• pinyuchen.com Pin-Yu Chen's Webpage
• zhangkaiyuan.com Kaiyuan Zhang @Purdue University