Summary of Identity Inference From Clip Models Using Only Textual Data, by Songze Li et al.
Identity Inference from CLIP Models using Only Textual Data
by Songze Li, Ruoxi Cheng, Xiaojun Jia
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 proposes a novel method called Textual Unimodal Detector (TUNI) to detect personally identifiable information (PII) in large-scale multimodal models like CLIP. The existing methods for identity inference require querying the model with full PII, including textual descriptions and corresponding images, which may lead to potential privacy breaches. TUNI addresses this challenge by developing a feature extraction algorithm guided by the CLIP model to extract features from text data only. This method leverages randomly generated textual gibberish to train anomaly detectors that determine if a person’s PII is in the training set or not. The paper demonstrates the superiority of TUNI over baselines, using various CLIP model architectures and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds ways to keep your personal information safe when using big computer models called CLIP. Right now, people are worried about how these models might share private info without permission. Some methods for keeping this from happening require using the full amount of private info, which isn’t good because it could reveal secrets. The solution proposed in this paper doesn’t need all that information and is better at finding hidden private data. |
Keywords
» Artificial intelligence » Feature extraction » Inference
