MLLM-as-a-Judge for Image Safety without Human Labeling

TL;DR

This paper introduces a novel zero-shot approach using pre-trained Multimodal Large Language Models (MLLMs) to identify unsafe images based on safety rules, eliminating the need for human-labeled data and enabling flexible, rule-based safety assessments.

Contribution

The authors propose a new MLLM-based method that objectifies safety rules, assesses relevance, and uses chain-of-thought reasoning to improve zero-shot image safety judgment accuracy.

Findings

High effectiveness in zero-shot safety judgment tasks

Outperforms traditional fine-tuning approaches

Reduces reliance on human-labeled datasets

Abstract

Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generation models are capable of producing harmful content, such as images containing sexual or violent material. Thus, it becomes crucial to identify such unsafe images based on established safety rules. Pre-trained Multimodal Large Language Models (MLLMs) offer potential in this regard, given their strong pattern recognition abilities. Existing approaches typically fine-tune MLLMs with human-labeled datasets, which however brings a series of drawbacks. First, relying on human annotators to label data following intricate and detailed guidelines is both expensive and labor-intensive. Furthermore, users of safety judgment systems may need to frequently update safety rules, making fine-tuning on human-based annotation more challenging. This raises the research question: Can we detect unsafe images by querying MLLMs in a zero-shot setting using a predefined safety constitution (a set of safety rules)? Our research showed that simply querying pre-trained MLLMs does not yield satisfactory results. This lack of effectiveness stems from factors such as the subjectivity of safety rules, the complexity of lengthy constitutions, and the inherent biases in the models. To address these challenges, we propose a MLLM-based method includes objectifying safety rules, assessing the relevance between rules and images, making quick judgments based on debiased token probabilities with logically complete yet simplified precondition chains for safety rules, and conducting more in-depth reasoning with cascaded chain-of-thought processes if necessary. Experiment results demonstrate that our method is highly effective for zero-shot image safety judgment tasks.