Summary of Inference Attacks Against Face Recognition Model Without Classification Layers, by Yuanqing Huang et al.
Inference Attacks Against Face Recognition Model without Classification Layers
by Yuanqing Huang, Huilong Chen, Yinggui Wang, Lei Wang
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed paper introduces a novel inference attack against face recognition (FR) models that do not have a classification layer. The attack consists of two stages: membership inference and model inversion. In the first stage, the authors analyze the distances between intermediate features and batch normalization parameters to determine whether a face image is from the training dataset or not. They then design an effective attack model that can perform this task. In the second stage, they use a pre-trained generative adversarial network (GAN) guided by the attack model in the first stage to reconstruct sensitive private data. This paper demonstrates the application of the proposed attack model in establishing privacy-preserving FR techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed an attack on face recognition models that don’t have a classification layer. They divided their attack into two parts: finding out if an image is from a training dataset or not, and then rebuilding private information using a special computer program. This is the first time someone has done this type of attack. |
Keywords
* Artificial intelligence * Batch normalization * Classification * Face recognition * Gan * Generative adversarial network * Inference
