Summary of Makeup-guided Facial Privacy Protection Via Untrained Neural Network Priors, by Fahad Shamshad et al.
Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priors
by Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar
First submitted to arxiv on: 20 Aug 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 A deep learning-based face recognition (FR) system poses significant privacy risks by tracking users without their consent. To mitigate this issue, recent facial privacy protection approaches embed adversarial noise into natural-looking makeup styles, but these methods require training on large-scale makeup datasets that are not always readily available. Moreover, they suffer from dataset bias, compromising protection efficacy for certain demographics. To address these limitations, we propose a test-time optimization approach that optimizes an untrained neural network to transfer makeup style from a reference to a source image in an adversarial manner. Our method includes two key modules: a correspondence module that aligns regions between reference and source images in latent space, and a decoder with conditional makeup layers. By optimizing the untrained decoder via carefully designed structural and makeup consistency losses, we generate a protected image that resembles the source but incorporates adversarial makeup to deceive FR models. Our approach does not rely on training with makeup face datasets, thus avoiding potential male/female dataset biases while providing effective protection. We extend our approach to videos by leveraging temporal correlations and demonstrate superior performance in face verification and identification tasks, as well as effectiveness against commercial FR systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being tracked without your consent just because someone recognizes you from a photo. That’s what’s happening with deep learning-based facial recognition systems. To protect our privacy, some experts suggest adding noise to the images so they can’t be recognized easily. However, this approach has its own problems, such as requiring lots of data and not working well for people who don’t fit the typical face shape or makeup style. A group of researchers came up with a new way to do this that doesn’t need all that extra data and works better for everyone. They took an ordinary neural network and taught it to transform one image into another, making sure the transformed image looks like the original but is harder to recognize by facial recognition systems. This approach not only protects our privacy but also works well with videos. The researchers tested their method and found that it outperformed other methods in recognizing faces and protecting privacy. |
Keywords
» Artificial intelligence » Decoder » Deep learning » Face recognition » Latent space » Neural network » Optimization » Tracking
