Ethical-Lens: Curbing Malicious Usages of Open-Source Text-to-Image Models

2024-04-18

arXiv.org

AI Summary

To address the misuse of open-source text-to-image models for generating toxic, biased, or otherwise harmful content, this paper proposes a lightweight, model-agnostic ethical alignment framework requiring no model modification or fine-tuning. Methodologically, it introduces a novel instruction-output co-correction mechanism: semantic-aware instruction rewriting coupled with post-hoc image refinement, enabling zero-shot, cross-model alignment. We further establish a multimodal, quantifiable evaluation suite covering toxicity, bias, safety, aesthetics, and fairness—integrating GPT-4V (for visual understanding), HEIM (for safety and aesthetic quality), and FairFace (for demographic fairness). Experiments demonstrate that our approach achieves ethical alignment performance on par with or exceeding commercial models such as DALL·E 3, while preserving image fidelity and generation quality. The implementation is fully open-sourced and designed for plug-and-play compatibility with mainstream open-source models including Stable Diffusion.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The burgeoning landscape of text-to-image models, exemplified by innovations such as Midjourney and DALLE 3, has revolutionized content creation across diverse sectors. However, these advancements bring forth critical ethical concerns, particularly with the misuse of open-source models to generate content that violates societal norms. Addressing this, we introduce Ethical-Lens, a framework designed to facilitate the value-aligned usage of text-to-image tools without necessitating internal model revision. Ethical-Lens ensures value alignment in text-to-image models across toxicity and bias dimensions by refining user commands and rectifying model outputs. Systematic evaluation metrics, combining GPT4-V, HEIM, and FairFace scores, assess alignment capability. Our experiments reveal that Ethical-Lens enhances alignment capabilities to levels comparable with or superior to commercial models like DALLE 3, ensuring user-generated content adheres to ethical standards while maintaining image quality. This study indicates the potential of Ethical-Lens to ensure the sustainable development of open-source text-to-image tools and their beneficial integration into society. Our code is available at https://github.com/yuzhu-cai/Ethical-Lens.

Problem

• Open-source text-to-image tools
• Ethical misuse
• Harmful content generation

Innovation

• Ethical-Lens
• Moral Optimization
• Bias Mitigation