RAVID: Retrieval-augmented Visual Detection: A Knowledge-driven Approach For Ai-generated Image Identification

Abstract

In this paper, we introduce RAVID, the first framework for AI-generated image detection that leverages visual retrieval-augmented generation (RAG). While RAG methods have shown promise in mitigating factual inaccuracies in foundation models, they have primarily focused on text, leaving visual knowledge underexplored. Meanwhile, existing detection methods, which struggle with generalization and robustness, often rely on low-level artifacts and model-specific features, limiting their adaptability. To address this, RAVID dynamically retrieves relevant images to enhance detection. Our approach utilizes a fine-tuned CLIP image encoder, RAVID CLIP, enhanced with category-related prompts to improve representation learning. We further integrate a vision-language model (VLM) to fuse retrieved images with the query, enriching the input and improving accuracy. Given a query image, RAVID generates an embedding using RAVID CLIP, retrieves the most relevant images from a database, and combines these

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keykeita2025ravid

Related papers

• Visual-rag: Benchmarking Text-to-image Retrieval Augmented Generation For Visual Knowledge Intensive Queries (2025)0.00
• Imagerag: Dynamic Image Retrieval For Reference-guided Image Generation (2025)0.00
• AR-RAG: Autoregressive Retrieval Augmentation For Image Generation (2025)0.00
• Cross-modal RAG: Sub-dimensional Text-to-image Retrieval-augmented Generation (2025)0.00
• RAVEN: Multitask Retrieval Augmented Vision-language Learning (2024)0.00
• Regionrag: Region-level Retrieval-augmented Generation For Visual Document Understanding (2025)0.00
• Visrag 2.0: Evidence-guided Multi-image Reasoning In Visual Retrieval-augmented Generation (2025)0.00
• SPARK-IL: Spectral Retrieval-augmented RAG For Knowledge-driven Deepfake Detection Via Incremental Learning (2026)0.00