Nov. 7, 2022, 2:20 a.m. | Lukas Struppek, Dominik Hintersdorf, Kristian Kersting

cs.CR updates on arXiv.org arxiv.org

While text-to-image synthesis currently enjoys great popularity among
researchers and the general public, the security of these models has been
neglected so far. Many text-guided image generation models rely on pre-trained
text encoders from external sources, and their users trust that the retrieved
models will behave as promised. Unfortunately, this might not be the case. We
introduce backdoor attacks against text-guided generative models and
demonstrate that their text encoders pose a major tampering risk. Our attacks
only slightly alter an …

artist backdoors image generation text

Financial Crimes Compliance - Senior - Consulting - Location Open

@ EY | New York City, US, 10001-8604

Software Engineer - Cloud Security

@ Neo4j | Malmö

Security Consultant

@ LRQA | Singapore, Singapore, SG, 119963

Identity Governance Consultant

@ Allianz | Sydney, NSW, AU, 2000

Educator, Cybersecurity

@ Brain Station | Toronto

Principal Security Engineer

@ Hippocratic AI | Palo Alto