Red-Teaming the Stable Diffusion Safety Filter

October 03, 2022 Β· Declared Dead Β· πŸ› arXiv.org

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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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