Summary of Hiding and Recovering Knowledge in Text-to-image Diffusion Models Via Learnable Prompts, by Anh Bui et al.
Hiding and Recovering Knowledge in Text-to-Image Diffusion Models via Learnable Prompts
by Anh Bui, Khanh Doan, Trung Le, Paul Montague, Tamas Abraham, Dinh Phung
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to address the issue of unwanted concepts in diffusion models trained on large-scale internet data. Specifically, it introduces a concept-hiding method that makes sensitive or harmful content inaccessible to public users while allowing controlled recovery when needed. The method involves incorporating a learnable prompt into the cross-attention module, acting as a secure memory that suppresses hidden concepts unless a secret key is provided. This enables flexible access control, ensuring that undesirable content cannot be easily generated while preserving the option to reinstate it under restricted conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a magic box that can generate amazing pictures from text descriptions, but sometimes it might produce things you don’t want to see. That’s what happens with some computer models when they’re trained on lots of internet data. They learn to make unwanted things like mean or offensive images. This paper shows how to fix this problem by creating a special filter that hides the bad stuff, so it can only be seen if you know a special password. This way, we can still use the magic box for good things, but keep the bad things hidden. |
Keywords
* Artificial intelligence * Cross attention * Diffusion * Prompt
