# reds-lab/Narcissus

<https://repo.masters-masters.com/en/repos/reds-lab/Narcissus>

[Home](https://repo.masters-masters.com/en) › [reds-lab](https://repo.masters-masters.com/en/owners/reds-lab) › [Narcissus](https://repo.masters-masters.com/en/repos/reds-lab/Narcissus)

reds-lab/Narcissus has 126 stars on GitHub. Its RepoMasters card: Prototype tier. The official implementation of the CCS'23 paper, Narcissus clean-label…

| Field | Value |
| --- | --- |
| Owner | [reds-lab](https://repo.masters-masters.com/en/owners/reds-lab) |
| Stars | 126 |
| Forks | 15 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | MIT |
| Created | 2022 |
| Topics | deep-, adversarial-attacks, adversarial-machine-learning, ai-security, backdoor-attacks, poisoning-attacks |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [reds-lab/Narcissus](https://github.com/reds-lab/Narcissus) |

## Repository

The official implementation of the CCS'23 paper, Narcissus clean-label backdoor attack -- only takes THREE images to poison a face recognition dataset in a clean-label way and achieves a 99.89% attack success rate.

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.