all InfoSec news
PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning. (arXiv:2205.06401v1 [cs.CR])
May 16, 2022, 1:20 a.m. | Hongbin Liu, Jinyuan Jia, Neil Zhenqiang Gong
cs.CR updates on arXiv.org arxiv.org
Contrastive learning pre-trains an image encoder using a large amount of
unlabeled data such that the image encoder can be used as a general-purpose
feature extractor for various downstream tasks. In this work, we propose
PoisonedEncoder, a data poisoning attack to contrastive learning. In
particular, an attacker injects carefully crafted poisoning inputs into the
unlabeled pre-training data, such that the downstream classifiers built based
on the poisoned encoder for multiple target downstream tasks simultaneously
classify attacker-chosen, arbitrary clean inputs as …
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Information Technology Specialist II: Network Architect
@ Los Angeles County Employees Retirement Association (LACERA) | Pasadena, CA
Cybersecurity Skills Challenge -- Sponsored by DoD
@ Correlation One | United States
Security Operations Center (SOC) Analyst
@ GK Cybersecurity Group | Remote
Engineering Manager - Cloud Security team
@ SentinelOne | Prague, Czech Republic
Legal & Compliance Apprentice (H/F)
@ Novo Nordisk | Puteaux, Île-de-France, FR
Manager, Governance Risk & Compliance
@ Comcast | Virtual