Safeguarding Multimodal Knowledge Copyright in the RAG-as-a-Service Environment
On the map: Retrieval-augmented generation → Privacy and security of RAG
also in 1 more
Abstract (arXiv)
As Retrieval-Augmented Generation (RAG) evolves into service-oriented platforms (Rag-as-a-Service) with shared knowledge bases, protecting the copyright of contributed data becomes essential. Existing watermarking methods in RAG focus solely on textual knowledge, leaving image knowledge unprotected. In this work, we propose AQUA, the first watermark framework for image knowledge protection in Multimodal RAG systems. AQUA embeds semantic signals into synthetic images using two complementary methods: acronym-based triggers and spatial relationship cues. These techniques ensure watermark signals survive indirect watermark propagation from image retriever to textual generator, being efficient, effective and imperceptible. Experiments across diverse models and datasets show that AQUA enables robust, stealthy, and reliable copyright tracing, filling a key gap in multimodal RAG protection.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation. Builds on 54 of its 77 references that are in the map. First posted on arXiv 10 Jun 2025.
Builds on
54 of 77 references.
Closest ancestors · 6
Same direction, newest first
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- 2025MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks25 Feb
- 2025Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs15 Feb
- 2025RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models9 Jan
- 2024Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation18 Oct
- 2024TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models22 May
Other references · 48
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36 more
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Built on by
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