Safe Text-to-Image Generation: Simply Sanitize the Prompt Embedding

• URL: http://arxiv.org/abs/2411.10329v1
• Date: Fri, 15 Nov 2024 16:29:02 GMT
• Title: Safe Text-to-Image Generation: Simply Sanitize the Prompt Embedding
• Authors: Huming Qiu, Guanxu Chen, Mi Zhang, Min Yang,
• Abstract summary: We propose a vision-agnostic safe generation framework, Embedding Sanitizer (ES) ES focuses on erasing inappropriate concepts from prompt embeddings and uses the sanitized embeddings to guide the model for safe generation. ES significantly outperforms existing safeguards in terms of interpretability and controllability while maintaining generation quality.
• Score: 13.481343482138888
• License:
• Abstract: In recent years, text-to-image (T2I) generation models have made significant progress in generating high-quality images that align with text descriptions. However, these models also face the risk of unsafe generation, potentially producing harmful content that violates usage policies, such as explicit material. Existing safe generation methods typically focus on suppressing inappropriate content by erasing undesired concepts from visual representations, while neglecting to sanitize the textual representation. Although these methods help mitigate the risk of misuse to certain extent, their robustness remains insufficient when dealing with adversarial attacks. Given that semantic consistency between input text and output image is a fundamental requirement for T2I models, we identify that textual representations (i.e., prompt embeddings) are likely the primary source of unsafe generation. To this end, we propose a vision-agnostic safe generation framework, Embedding Sanitizer (ES), which focuses on erasing inappropriate concepts from prompt embeddings and uses the sanitized embeddings to guide the model for safe generation. ES is applied to the output of the text encoder as a plug-and-play module, enabling seamless integration with different T2I models as well as other safeguards. In addition, ES's unique scoring mechanism assigns a score to each token in the prompt to indicate its potential harmfulness, and dynamically adjusts the sanitization intensity to balance defensive performance and generation quality. Through extensive evaluation on five prompt benchmarks, our approach achieves state-of-the-art robustness by sanitizing the source (prompt embedding) of unsafe generation compared to nine baseline methods. It significantly outperforms existing safeguards in terms of interpretability and controllability while maintaining generation quality.

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