arXiv (All)
open access
Improving Generalizability and Undetectability for Targeted Adversarial Attacks on Multimodal Pre-trained Models
Multimodal pre-trained models (e.g., ImageBind), which align distinct data modalities into a shared embedding space, have shown remarkable success across downstream tasks. However, their increasing adoption raises serious security concerns, especially regarding targeted adversarial attacks. In this
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Automatic Red Teaming for Implicit Vulnerabilities of Text-to-Image Models#973701
- PHANTOM: A Large-Scale Dataset of Multimodal Adversarial Attacks for Vision-Language Models#303213
- Render Before Reading: Visual Rendering as a Prompt Injection Defense#1130963
- Render Before Reading: Visual Rendering as a Prompt Injection Defense#1122234
- A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalities#972564
Record · ID 972958
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive