Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?
Do Vision-Language Models Respect Contextual Integrity in Location Disclosure? explores A privacy-focused tool using VLMs to manage geolocation disclosure by respecting contextual integrity.. Commercial viability score: 6/10 in Privacy in AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisGeoRC: A Benchmark for Geolocation Reasoning ChainsViability: 5.0
- Builds On ThisThe Dual Mechanisms of Spatial Reasoning in Vision-Language ModelsViability: 3.0
- Builds On ThisLost in Space? Vision-Language Models Struggle with Relative Camera Pose EstimationViability: 5.0
- Higher ViabilityLoc3R-VLM: Language-based Localization and 3D Reasoning with Vision-Language ModelsViability: 8.0
- Higher ViabilityDo LLMs Know What Is Private Internally? Probing and Steering Contextual Privacy Norms in Large Language Model Representations