Latent Guard: a Safety Framework for Text-to-image Generation

• URL: http://arxiv.org/abs/2404.08031v2
• Date: Sun, 18 Aug 2024 18:53:16 GMT
• Title: Latent Guard: a Safety Framework for Text-to-image Generation
• Authors: Runtao Liu, Ashkan Khakzar, Jindong Gu, Qifeng Chen, Philip Torr, Fabio Pizzati,
• Abstract summary: Existing safety measures are either based on text blacklists, which can be easily circumvented, or harmful content classification. We propose Latent Guard, a framework designed to improve safety measures in text-to-image generation. Inspired by blacklist-based approaches, Latent Guard learns a latent space on top of the T2I model's text encoder, where it is possible to check the presence of harmful concepts.
• Score: 64.49596711025993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the ability to generate high-quality images, text-to-image (T2I) models can be exploited for creating inappropriate content. To prevent misuse, existing safety measures are either based on text blacklists, which can be easily circumvented, or harmful content classification, requiring large datasets for training and offering low flexibility. Hence, we propose Latent Guard, a framework designed to improve safety measures in text-to-image generation. Inspired by blacklist-based approaches, Latent Guard learns a latent space on top of the T2I model's text encoder, where it is possible to check the presence of harmful concepts in the input text embeddings. Our proposed framework is composed of a data generation pipeline specific to the task using large language models, ad-hoc architectural components, and a contrastive learning strategy to benefit from the generated data. The effectiveness of our method is verified on three datasets and against four baselines. Code and data will be shared at https://latentguard.github.io/.

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