43 citations · 50 across the 7 of their papers we have counts for
7 papers
MYCROFT: Towards Effective and Efficient External Data Augmentation
Zain Sarwar, Van Tran, Arjun Nitin Bhagoji +3
Machine learning (ML) models often require large amounts of data to perform well. When the available data is limited, model trainers may need to acquire more data from external sou…
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker
Sihui Dai, Wenxin Ding, Arjun Nitin Bhagoji +4
Finding classifiers robust to adversarial examples is critical for their safe deployment. Determining the robustness of the best possible classifier under a given threat model for…
Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models
Shawn Shan, Jenna Cryan, Emily Wenger +3
Recent text-to-image diffusion models such as MidJourney and Stable Diffusion threaten to displace many in the professional artist community. In particular, models can learn to mim…
Data Isotopes for Data Provenance in DNNs
Emily Wenger, Xiuyu Li, Ben Y. Zhao +1
Today, creators of data-hungry deep neural networks (DNNs) scour the Internet for training fodder, leaving users with little control over or knowledge of when their data is appropr…
Natural Backdoor Datasets
Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji +4
Extensive literature on backdoor poison attacks has studied attacks and defenses for backdoors using "digital trigger patterns." In contrast, "physical backdoors" use physical obje…
Understanding Robust Learning through the Lens of Representation Similarities
Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag +3
Representation learning, i.e. the generation of representations useful for downstream applications, is a task of fundamental importance that underlies much of the success of deep n…
On the Permanence of Backdoors in Evolving Models
Huiying Li, Arjun Nitin Bhagoji, Yuxin Chen +2
Existing research on training-time attacks for deep neural networks (DNNs), such as backdoors, largely assume that models are static once trained, and hidden backdoors trained into…