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<!DOCTYPE html>
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content="Automatic Red-Teaming for T2I Diffusion Models">
<meta name="keywords" content="RingABell, Diffusion, Red-Teaming">
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<title>Ring-A-Bell! How Reliable are Concept Removal Methods For Diffusion Models?</title>
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<!-- Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie, Chih-Hsun Lin, Jia-You Chen, Bo Li, Pin-Yu Chen, Chia-Mu Yu, Chun-Ying Huang --->
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<h1 class="title is-1 publication-title">Ring-A-Bell! How Reliable are Concept Removal Methods For Diffusion Models?</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="#">Yu-Lin Tsai</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://chiayihsu.weebly.com/">Chia-Yi Hsu</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://alphapav.github.io/">Chulin Xie</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="#">Chih-Hsun Lin</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="#">Jia-You Chen</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://aisecure.github.io/">Bo Li</a><sup>2, 3</sup>,
</span>
<span class="author-block">
<a href="https://sites.google.com/site/pinyuchenpage/home">Pin-Yu Chen</a><sup>4</sup>
</span>
<span class="author-block">
<a href="https://chiamuyu.weebly.com/">Chia-Mu Yu</a><sup>1</sup>
</span>
<span class="author-block">
<a href="https://people.cs.nycu.edu.tw/~chuang/">Chun-Ying Huang</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>National Yang Ming Chiao Tung University,</span>
<span class="author-block"><sup>2</sup>University of Illinois at Urbana Champaign,</span>
<span class="author-block"><sup>3</sup>University of Chicago</span>
<span class="author-block"><sup>4</sup>IBM Research</span>
</div>
<div class="is-size-5 publication-authors">
<span class = "author-block"> ICLR 2024</span>
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<img src="./static/images/OnlineServiceAttackImg.png" class = "center" alt="Demonstrations of Ring-A-Bell" style="width:2000px;height:400px;">
<h2 class="subtitle has-text-centered">
<span class="dnerf">Ring-A-Bell</span> evaluates(red-teaming) T2I diffusion model in a black-box fashion.
</h2>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
We present an effective black-box attack/evaluation framework for common T2I diffusion models, including Stable Diffusion, DALLE, MidJourney.
</p>
<p>
We introduce Ring-A-Bell, a model-agnostic red-teaming tool for T2I diffusion models, where
the whole evaluation can be prepared in advance without prior knowledge of the
target model.
Specifically, Ring-A-Bell first performs concept extraction to obtain holistic representations for sensitive and inappropriate concepts. Subsequently, by leveraging the extracted concept, Ring-A-Bell automatically identifies problematic prompts for diffusion models with the corresponding generation of inappropriate content, allowing the user to assess the reliability of deployed safety mechanisms.
Finally, we empirically validate our method by testing online services such as Midjourney and various methods of concept removal.
</p>
<p>
Our results show that <span class="dnerf">Ring-A-Bell</span>, by manipulating safe prompting benchmarks,
can transform prompts that were originally regarded as safe to evade existing safety mechanisms, thus revealing the defects of the so-called safety mechanisms which could practically lead to the generation of harmful contents. In essence, Ring-A-Bell
could serve as a red-teaming tool to understand the limitations of deployed safety mechanisms and to explore the risk under plausible attacks.
</p>
<b style='color:red;'>CAUTION: This paper includes model-generated content that may contain offensive or distressing material</b>
</div>
</div>
</div>
<!--/ Abstract. -->
</div>
</section>
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<h2 class= "title is-3">Model Architecture</h2>
<img src="./static/images/model_architecture.png" alt="Model Architecture of Ring-A-Bell" style="width:1800px;height:400px;">
<h2 class="subtitle has-text-centered">
<span class="dnerf">Ring-A-Bell</span> transforms benign prompts into harmful ones to create inappropriate images.
</h2>
<p>
The rationale behind Ring-A-Bell
is that current T2I models with safety mechanisms either learn to disassociate or simply filter out
relevant words of the target concepts with their representation c, and thus the detection or removal of such concepts may not be carried out completely if there exist implicit text-concept associations
embedded in the T2I generation process. That is, Ring-A-Bell aims to test whether a supposedly
removed concept can be revoked via our prompt optimization procedure.
</p>
</div>
</div>
<!--/ Matting. -->
<!-- Performance. -->
<section class="hero">
<div class="columns is-centered">
<div class="column is-small is-full-width is-light">
<h2 class="title is-3">Performance</h2>
<!-- Concept Removal. -->
<h3 class="title is-4">Evaluation against Concept Removal Models - Nudity Concept</h3>
<div class="content has-text-justified">
<p>
Ring-A-Bell can effectively search for problematic prompts that are not eliminated by concept removal methods. Thereby increasing the chance of generating inappropriate images and exposing the vulnerabilities of such methods.
</p>
</div>
<div class = "column is-full-width">
<img src="./static/images/nudity.png" alt="Nudity Concept Generation" style="width:1800px;height:600px;">
<h2 class="subtitle has-text-centered">
<span class="dnerf">Ring-A-Bell</span> actively search for nudity concepts in order to generate nudity-related images.
</h2>
</div>
<br/>
<!--/ Concept Removal. -->
<!-- Concept Removal II -->
<h3 class="title is-4">Evaluation against Concept Removal Models - Artistic Concept</h3>
<div class="content has-text-justified">
<p>
Ring-A-Bell can as well search for underlying artistic concept, and manipulate the safety-enhanced diffusion models to generate plausible copyrighted content.
</p>
</div>
<div class = "column is-full-width">
<img src="./static/images/vangogh.png" alt="Artistic Concept" style="width:1800px;height:600px;">
<h2 class="subtitle has-text-centered">
<span class="dnerf">Ring-A-Bell</span> actively search for Van Gogh concepts in order to generate copyrighted images.
</h2>
</div>
<!-- /Concept Removal II -->
<!-- Concept Removal III -->
<h3 class="title is-4">Evaluation against Concept Removal Models - Specific Object</h3>
<div class="content has-text-justified">
<p>
Ring-A-Bell can also search for problematic prompts to generated specific model-prohibited object.
</p>
</div>
<div class = "column is-full-width">
<img src="./static/images/car.png" alt="Specific Object" style="width:1800px;height:600px;">
<h2 class="subtitle has-text-centered">
<span class="dnerf">Ring-A-Bell</span> actively search for remnant concept in order to generate specific object such as car.
</h2>
</div>
<!-- /Concept Removal III -->
</div>
</div>
</section>
<section class="hero teaser">
<!-- Online I -->
<h3 class="title is-4">Evaluation against Online APIs - Additional Results</h3>
<div class="content has-text-justified">
<p>
Ring-A-Bell can also search for problematic prompts for blackbox APIs.
</p>
</div>
<div class = "is-max-desktop">
<img src="./static/images/OnlineServiceAttackImg2.png" alt="Specific Object" style="width:2000px;height:400px;">
<h2 class="subtitle has-text-centered">
<span class="dnerf">Ring-A-Bell</span> actively search for nudity and violence concepts even facing blackbox APIs.
</h2>
</div>
<!-- /Online I -->
</section>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{ringabell,
title={Ring-A-Bell! How Reliable are Concept Removal Methods For Diffusion Models?},
author={Yu-Lin Tsai*, Chia-Yi Hsu*, Chulin Xie, Chih-Hsun Lin, Jia-You Chen, Bo Li, Pin-Yu Chen, Chia-Mu Yu, Chun-Ying Huang},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=lm7MRcsFiS}
}</code></pre>
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