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--- |
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task_categories: |
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- feature-extraction |
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- image-classification |
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- zero-shot-image-classification |
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pretty_name: Perturbed Faces |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Perturbed Faces |
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This dataset contains 1000 images from [CelebA dataset](!https://www.kaggle.com/datasets/jessicali9530/celeba-dataset). For each of the thousand images dataset also has [LowKey](https://openreview.net/forum?id=hJmtwocEqzc) perturbed version and [Fawkes](https://sandlab.cs.uchicago.edu/fawkes/) perturbed version. |
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LowKey and Fawkes perturbed images have _attacked & _cloaked at the end of the filename respectively. |
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| File Name | Version | |
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|---------------------|--------------------------| |
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| 000001.jpg | Original | |
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| 000001_cloaked.png | Fawkes perturbed version | |
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| 000001_attacked.png | LowKey perturbed version | |
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The Fawkes perturbed images are created using CLI provided in the [github repository](https://github.com/Shawn-Shan/fawkes) with protection mode set to mid. The LowKey version of |
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images are created using Python code provided with the paper. |
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## Citation |
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If you found this work helpful for your research, please cite it as following: |
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``` |
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@misc{2301.07315, |
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Author = {Aaditya Bhat and Shrey Jain}, |
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Title = {Face Recognition in the age of CLIP & Billion image datasets}, |
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Year = {2023}, |
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Eprint = {arXiv:2301.07315}, |
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} |
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``` |