PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models (2501.03544v1)

Abstract: Text-to-image (T2I) models have been shown to be vulnerable to misuse, particularly in generating not-safe-for-work (NSFW) content, raising serious ethical concerns. In this work, we present PromptGuard, a novel content moderation technique that draws inspiration from the system prompt mechanism in LLMs for safety alignment. Unlike LLMs, T2I models lack a direct interface for enforcing behavioral guidelines. Our key idea is to optimize a safety soft prompt that functions as an implicit system prompt within the T2I model's textual embedding space. This universal soft prompt (P*) directly moderates NSFW inputs, enabling safe yet realistic image generation without altering the inference efficiency or requiring proxy models. Extensive experiments across three datasets demonstrate that PromptGuard effectively mitigates NSFW content generation while preserving high-quality benign outputs. PromptGuard achieves 7.8 times faster than prior content moderation methods, surpassing eight state-of-the-art defenses with an optimal unsafe ratio down to 5.84%.

Soft Prompt-Guided Unsafe Content Moderation in Text-to-Image Models: An Analysis of P-Guard

The paper "P-Guard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models" presents an innovative methodology aimed at addressing the pervasive issue of generating unsafe content in text-to-image (T2I) models. These models, such as Stable Diffusion, have introduced groundbreaking capabilities in visual synthesis by allowing the creation of images from text prompts. However, they are often exploited in generating not-safe-for-work (NSFW) content, which poses significant ethical concerns. The authors propose a solution called P-Guard, leveraging a system similar to prompt mechanisms in LLMs that ensure ethical alignment during output generation.

Methodology Overview

P-Guard introduces a novel approach by optimizing a soft prompt that acts as an implicit system prompt within the T2I model's textual embedding space. This mechanism is designed to intervene directly in moderating NSFW inputs, establishing a safe content generation pathway without sacrificing the quality of benign outputs or the inference efficiency. Unlike existing models that require adjustments in the model's parameters or external moderation models that impose computationally expensive processes, P-Guard maintains the integrity of the model's inference by guiding it through an optimized soft prompt, thus avoiding additional overhead.

Two core challenges addressed are: establishing a method to guide safety prompts without altering the T2I model's architecture or parameters, and ensuring that the moderation is universally effective across diverse types of NSFW content. The solution involves the optimization of a safety pseudo-word into a soft prompt in the textual embedding space. Further refinement is achieved using a divide-and-conquer strategy to create type-specific safety prompts that are later combined to strengthen protection across various NSFW categories such as sexual, violent, political, and disturbing content.

Empirical Evaluation

The researchers rigorously evaluated P-Guard using extensive experiments across multiple datasets and compared it against eight state-of-the-art defenses. P-Guard significantly outperformed all other baselines, achieving an unsafe ratio as low as 5.84%. This is a notable improvement considering the broad categories of NSFW content tested, including sexually explicit, violent, political, and disturbing types.

Notably, P-Guard's efficiency is underscored by its inference operating 7.8 times faster than existing methods, which typically require substantial time and computational resources due to additional model structures or iterative diffusion process modifications. In terms of benign content preservation, P-Guard also demonstrated robustness, maintaining visual quality and textual alignment better than most contemporary methods, as evidenced by high CLIP and low LPIPS scores.

Theoretical and Practical Implications

Theoretically, the insights gained from the paper extend the potential of properly aligned text-to-image models by showcasing that ethical constraints and creative outputs are not mutually exclusive. The soft prompt tuning approach could serve as a precursor to more sophisticated alignment techniques in the future, paving the way for broader applications across different generative AI models, including text-to-video and image-to-image transformations.

Practically, the deployment of such techniques can be integral in mitigating misuse in digital content creation platforms, addressing ethical concerns by proactively preventing the dissemination of harmful content. The method's adaptability also allows it to be easily integrated across various models sharing similar encoding frameworks, offering versatility and significant impact potential in real-world implementations.

Future Directions

Despite its success, there exist areas for future exploration, such as extending P-Guard to accommodate evolving standards of safety and ethical use across diverse cultural landscapes. Moreover, the team points out the necessity for real-world user involvement to refine the efficacy and applicability of NSFW detection systems, constrained here due to ethical considerations surrounding the exposure to potentially harmful content.

In summary, P-Guard emerges as a highly effective, lightweight, and translatable solution in content moderation for text-to-image models, marking a step forward in aligning generative AI with societal ethics and expectations. As these technologies pervade more aspects of digital media, such responsible implementations will be increasingly vital.