Summary of Embedding Space Selection For Detecting Memorization and Fingerprinting in Generative Models, by Jack He et al.
Embedding Space Selection for Detecting Memorization and Fingerprinting in Generative Models
by Jack He, Jianxing Zhao, Andrew Bai, Cho-Jui Hsieh
First submitted to arxiv on: 30 Jul 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 The paper explores the memorization capabilities of Generative Adversarial Networks (GANs) and Diffusion Models, which are essential technologies in various fields including art creation and healthcare. The study focuses on measuring distances between samples in embedding spaces to detect data memorization, a significant challenge for these models. Notably, Vision Transformers (ViTs) exhibit a trend where deeper layers show less memorization. Early layers’ embeddings are more sensitive to low-level memorization, while latter layers are more sensitive to high-level memorization. The study introduces a unique fingerprinting methodology based on the memorization scores across different ViT layers, enhancing identification accuracy by 30% compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative models like GANs and Diffusion Models are important in many areas, including art and healthcare. But these models can remember data they shouldn’t, which is bad for privacy and makes the generated content not trustworthy. The study looks at how well these models do this memorization by measuring distances between samples in their “embedding spaces”. It finds that some layers of Vision Transformers are better at hiding what they know than others. This helps create a new way to identify when these models are used to make fake things like deepfakes. |
Keywords
* Artificial intelligence * Diffusion * Vit
