Latent Guard: A Safety Framework for Detecting Unsafe Concepts in Text-to-Image Generation

מושגי ליבה

Latent Guard is a framework designed to efficiently detect the presence of blacklisted concepts in text-to-image generation input prompts, enabling robust safety measures without the need for expensive retraining.

תקציר

The paper introduces Latent Guard, a novel safety framework for text-to-image (T2I) generation models. Existing safety measures for T2I models are either based on text blacklists, which can be easily circumvented, or require large datasets for training harmful content classification, offering low flexibility.
Latent Guard proposes a different approach, focusing on detecting the presence of blacklisted concepts in the latent representation of input prompts, rather than directly classifying prompts as safe or unsafe. The key components are:

Data Generation Pipeline: The authors create a dataset called CoPro, which includes safe and unsafe prompts centered around a set of blacklisted concepts. This data is generated using large language models.

Embedding Mapping Layer: Latent Guard uses a trainable architectural component on top of a pre-trained text encoder to extract latent representations of input prompts and blacklisted concepts. This layer employs multi-head cross-attention to focus on the relevant tokens in the prompt.

Contrastive Training: Latent Guard is trained using a contrastive learning strategy, which maps the latent representations of unsafe prompts and their corresponding blacklisted concepts close together, while separating them from safe prompts.

During inference, Latent Guard efficiently checks the cosine similarity between the latent representation of the input prompt and the pre-computed embeddings of blacklisted concepts. If any similarity exceeds a threshold, the prompt is blocked, preventing the generation of unsafe content.
The authors thoroughly evaluate Latent Guard on the CoPro dataset and existing datasets, demonstrating its effectiveness in detecting unsafe prompts, including those with adversarial attacks targeting the text encoder. Latent Guard also offers the flexibility to update the blacklist of concepts at test time without retraining.

סטטיסטיקה

Latent Diffusion Models [27] perform the diffusion process in an autoencoder latent space, significantly lowering computational requirements.
The authors use an uncensored version of Mixtral 8x7B for generating data.
The authors use the CLIP Transformer [24] as the text encoder, which is also employed in Stable Diffusion v1.5 [27] and SDXL [21].

ציטוטים

"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."

תובנות מפתח מזוקקות מ:

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

by Runtao Liu,A... ב- arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08031.pdf

שאלות מעמיקות

How can Latent Guard be extended to handle more complex safety requirements, such as detecting the presence of harmful visual content in generated images?

Latent Guard can be extended to handle more complex safety requirements by incorporating additional modules for image analysis. One approach could be to integrate a visual content recognition system that analyzes the generated images for harmful visual content. This system could use computer vision techniques to identify objects, scenes, or patterns that are considered inappropriate or unsafe. By combining text analysis with image analysis, Latent Guard can provide a comprehensive safety framework for text-to-image generation. Additionally, the framework could be enhanced with advanced image processing algorithms to detect subtle cues or hidden content that may not be immediately apparent.

What are the potential limitations of the contrastive learning approach used in Latent Guard, and how could it be further improved to enhance the robustness of the concept detection?

One potential limitation of the contrastive learning approach in Latent Guard is the reliance on predefined blacklisted concepts. This approach may struggle with detecting new or evolving harmful concepts that are not included in the initial blacklist. To address this limitation, the contrastive learning strategy could be augmented with a self-supervised learning component that continuously updates the concept embeddings based on emerging trends or user feedback. By dynamically adjusting the concept embeddings during inference, Latent Guard can adapt to changing safety requirements and improve the robustness of concept detection.

Given the flexibility of Latent Guard to update the blacklist of concepts at test time, how could this capability be leveraged to address emerging safety concerns in text-to-image generation as the technology continues to evolve?

The capability of Latent Guard to update the blacklist of concepts at test time provides a powerful tool for addressing emerging safety concerns in text-to-image generation. This flexibility can be leveraged by integrating real-time monitoring systems that track online content trends and user interactions. By analyzing user inputs and feedback, Latent Guard can dynamically adjust the blacklist to block newly identified harmful concepts or patterns. Additionally, the framework can be enhanced with machine learning models that learn from user interactions to proactively identify and prevent the generation of inappropriate content. This adaptive approach ensures that Latent Guard stays ahead of emerging safety concerns and maintains a high level of protection in text-to-image generation applications.