Papers › Unlearning Targeted Information via Single Layer Unlearning Gradient

Unlearning Targeted Information via Single Layer Unlearning Gradient

16 Jul 2024arXiv:2407.11867archive 2025-07-28

Zikui Cai, Yaoteng Tan, M. Salman Asif

Unauthorized privacy-related and copyrighted content generation using generative-AI is becoming a significant concern for human society, raising ethical, legal, and privacy issues that demand urgent attention. The EU's General Data Protection Regulation (GDPR) include a "right to be forgotten," which allows individuals to request the deletion of their personal data. However, this primarily applies to data stored in traditional databases, not AI models. Recently, machine unlearning techniques have arise that attempt to eliminate the influence of sensitive content used during AI model training, but they often require extensive updates to the deployed systems and incur substantial computational costs. In this work, we propose a novel and efficient method called Single Layer Unlearning Gradient (SLUG), that can unlearn targeted information by updating targeted layers of a model using a one-time gradient computation. Our method is highly modular and enables the selective removal of multiple sensitive concepts, such as celebrity names and copyrighted content, from the generated outputs of widely used foundation models (e.g., CLIP) and generative models (e.g., Stable Diffusion). Broadly, our method ensures AI-generated content complies with privacy regulations and intellectual property laws, fostering responsible use of generative models, mitigating legal risks and promoting a trustworthy, socially responsible AI ecosystem.

Code

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