Summary of Pre-trained Encoder Inference: Revealing Upstream Encoders in Downstream Machine Learning Services, by Shaopeng Fu et al.
Pre-trained Encoder Inference: Revealing Upstream Encoders In Downstream Machine Learning Services
by Shaopeng Fu, Xuexue Sun, Ke Qing, Tianhang Zheng, Di Wang
First submitted to arxiv on: 5 Aug 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 reveals a previously unknown vulnerability in pre-trained encoders, dubbed the Pre-trained Encoder Inference (PEI) attack. The PEI attack targets downstream machine learning services that use these encoders without providing direct access to the encoder itself. By only accessing an API and a set of candidate encoders, the attacker can infer which encoder is being used by the targeted service. Experiments show that the PEI attack successfully reveals the hidden encoder in most cases, even when it’s not in the candidate set. The paper also demonstrates the effectiveness of this attack on real-world tasks such as image classification and text-to-image generation. Furthermore, a case study on the LLaVA vision-language model illustrates how the PEI attack can be used to assist other machine learning attacks, like adversarial attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper shows that even if someone uses a powerful pre-trained AI model without sharing it with others, they can still figure out which model is being used by analyzing its behavior. This is concerning because it means the privacy of these models could be compromised. The researchers demonstrated their technique on three different tasks and found that it often works even when the target model isn’t one of the options they were given. |
Keywords
» Artificial intelligence » Encoder » Image classification » Image generation » Inference » Language model » Machine learning
