Authors: Shengwei An, Sheng-Yen Chou, Kaiyuan Zhang, Qiuling Xu, Guanhong Tao, Guangyu Shen, Siyuan Cheng, Shiqing Ma, Pin-Yu Chen, Tsung-Yi Ho, Xiangyu Zhang

Published on: November 27, 2023

Impact Score: 8.45

Arxiv code: Arxiv:2312.0005

Summary

• What is new: Introduces the first backdoor detection and removal framework for Diffusion Models (DMs), named Elijah.
• Why this is important: Diffusion models are vulnerable to backdoor attacks, which can cause them to generate inappropriate images when triggered.
• What the research proposes: The Elijah framework for detecting and removing backdoors from DMs without significantly impacting their utility.
• Results: Elijah achieved nearly 100% detection accuracy and effectively neutralized backdoor effects with minimal impact on model utility.

Technical Details

Technological frameworks used: Elijah

Models used: DDPM, NCSN, LDM

Data used: 13 samplers against 3 existing backdoor attacks

Potential Impact

Companies in digital media, advertising, and security software markets could benefit from these insights, while malicious actors could be disrupted.

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