Mitigating Unsafe Content Generation in Text-to-Image Models

核心概念

SAFEGEN, a text-agnostic framework, can effectively mitigate the generation of sexually explicit content by text-to-image models, even under adversarial prompts, by removing unsafe visual representations from the model.

摘要

The paper presents SAFEGEN, a text-agnostic framework to mitigate the generation of unsafe (sexually explicit) content by text-to-image (T2I) models. The key idea is to eliminate unsafe visual representations from the model, regardless of the text input, in order to make the T2I model resistant to adversarial prompts.

The paper first analyzes the limitations of existing defenses, which mainly focus on filtering inappropriate inputs/outputs or suppressing improper text embeddings. These methods can be bypassed by adversarial prompts that appear innocent but are ill-intended.

To address this, the paper proposes SAFEGEN, which regulates the vision-only self-attention layers of the T2I model to remove the unsafe image generation capability. This is achieved by using <nude, censored, benign> image triplets to edit the self-attention layers, without interfering with the text-dependent components.

Extensive experiments on four datasets, including adversarial prompts, demonstrate that SAFEGEN outperforms eight state-of-the-art baselines and achieves 99.1% sexual content removal performance, while preserving the high-fidelity of benign images. The paper also shows that SAFEGEN can complement and integrate with existing text-dependent defenses to further enhance the overall safety of T2I models.

统计

The original SD-V1.4 model produces a total of 6,403 exposed body parts on the NSFW-56k dataset.
SAFEGEN reduces this number to 58, achieving a 99.1% NSFW removal rate.
Compared to baselines, SAFEGEN consistently achieves the lowest CLIP scores across all adversarial prompt datasets, indicating its effectiveness in reducing the text-to-image alignment.

引用

"SAFEGEN regulates the vision-only self-attention layers to remove the unsafe image generation capability from an already-trained T2I model."
"Extensive experiments conducted on four datasets demonstrate SAFEGEN's effectiveness in mitigating unsafe content generation while preserving the high-fidelity of benign images."

从中提取的关键见解

SafeGen

by Xinfeng Li,Y... 在 arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06666.pdf

更深入的查询

How can SAFEGEN's text-agnostic approach be extended to other generative models beyond text-to-image, such as text-to-video or audio-to-image

SAFEGEN's text-agnostic approach can be extended to other generative models beyond text-to-image by adapting the methodology to suit the specific requirements of text-to-video or audio-to-image models. For text-to-video models, the self-attention mechanism can be modified to focus on the visual representations in the generated videos, allowing for the removal of unsafe content regardless of the text input. This can involve adjusting the attention matrices to identify and suppress explicit visual elements in the generated videos. Similarly, for audio-to-image models, the self-attention layers can be tailored to target unsafe visual representations based on the audio input, ensuring that the generated images do not contain inappropriate content. By customizing the self-attention mechanism in these models, SAFEGEN's text-agnostic approach can effectively mitigate unsafe content generation in various generative models.

What are the potential ethical concerns and societal implications of deploying SAFEGEN in real-world applications, and how can they be addressed

The deployment of SAFEGEN in real-world applications raises several ethical concerns and societal implications that need to be addressed. One major concern is the potential impact on freedom of expression and creativity, as the system may inadvertently censor or restrict certain types of content, even if it is not explicitly harmful. This could lead to issues of censorship and stifling of artistic expression. Additionally, there is a risk of bias in the system's decision-making process, as it may inadvertently target specific groups or types of content based on preconceived notions of what constitutes "unsafe" content. This could result in discriminatory outcomes and reinforce existing biases in the data used to train the model.
To address these concerns, it is essential to ensure transparency and accountability in the deployment of SAFEGEN. This includes providing clear guidelines on the types of content that are considered unsafe and the reasoning behind the system's decisions. Regular audits and evaluations should be conducted to monitor the system's performance and identify any biases or errors. Moreover, involving diverse stakeholders, including content creators, ethicists, and community representatives, in the development and implementation of SAFEGEN can help mitigate potential ethical issues and ensure that the system aligns with societal values and norms.

Given the rapid evolution of AI-generated content, how can SAFEGEN's design be made more adaptable to handle emerging forms of unsafe content in the future

To make SAFEGEN's design more adaptable to handle emerging forms of unsafe content in the future, several strategies can be implemented. Firstly, continuous monitoring and updating of the system's algorithms and parameters based on evolving trends and patterns in unsafe content generation can help ensure its effectiveness in detecting and mitigating new forms of inappropriate content. This can involve incorporating advanced machine learning techniques, such as reinforcement learning, to enable the system to adapt and learn from new data and scenarios.
Additionally, establishing a feedback loop with users and content moderators can provide valuable insights into emerging threats and help refine SAFEGEN's capabilities in real-time. By collecting feedback on the system's performance and incorporating user input on potential areas of improvement, SAFEGEN can stay ahead of emerging challenges in unsafe content generation. Furthermore, collaborating with industry experts, researchers, and regulatory bodies to stay informed about the latest developments in AI-generated content and regulatory requirements can enhance SAFEGEN's adaptability and responsiveness to changing content landscapes.