Summary of Network Inversion For Training-like Data Reconstruction, by Pirzada Suhail et al.
Network Inversion for Training-Like Data Reconstruction
by Pirzada Suhail, Amit Sethi
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 Machine Learning models are often trained on proprietary and private data that cannot be shared, though the trained models themselves are distributed openly assuming that sharing model weights is privacy preserving, as training data is not expected to be inferred from the model weights. This paper presents Training-Like Data Reconstruction (TLDR), a network inversion-based approach to reconstruct training-like data from trained models. The authors introduce a comprehensive network inversion technique that learns the input space corresponding to different classes in the classifier using a single conditioned generator. They modify the inversion process to incentivize the generator to reconstruct training-like data by exploiting key properties of the classifier with respect to the training data and some prior knowledge about the images. To validate their approach, they conduct empirical evaluations on multiple standard vision classification datasets, highlighting potential privacy risks involved in sharing machine learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine Learning models are often trained using private data that can’t be shared. But what if someone could recreate that private data just by looking at the model itself? This paper shows how to do just that! They created a special technique called Training-Like Data Reconstruction (TLDR) that takes a trained model and turns it back into the original training data. This helps show how sharing models can actually put people’s privacy at risk. |
Keywords
* Artificial intelligence * Classification * Machine learning
