Red-Teaming the Stable Diffusion Safety Filter
October 03, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Javier Rando, Daniel Paleka, David Lindner, Lennart Heim, Florian Tramèr
arXiv ID
2210.04610
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.CR,
cs.CV,
cs.CY,
cs.LG
Citations
260
Venue
arXiv.org
Last Checked
3 months ago
Abstract
Stable Diffusion is a recent open-source image generation model comparable to proprietary models such as DALLE, Imagen, or Parti. Stable Diffusion comes with a safety filter that aims to prevent generating explicit images. Unfortunately, the filter is obfuscated and poorly documented. This makes it hard for users to prevent misuse in their applications, and to understand the filter's limitations and improve it. We first show that it is easy to generate disturbing content that bypasses the safety filter. We then reverse-engineer the filter and find that while it aims to prevent sexual content, it ignores violence, gore, and other similarly disturbing content. Based on our analysis, we argue safety measures in future model releases should strive to be fully open and properly documented to stimulate security contributions from the community.
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