Research Paper#Computer Vision, Image Generation, Anonymization🔬 ResearchAnalyzed: Jan 3, 2026 19:22
Reverse Personalization for Face Anonymization
Published:Dec 28, 2025 16:06
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of anonymizing facial images generated by text-to-image diffusion models. It introduces a novel 'reverse personalization' framework that allows for direct manipulation of images without relying on text prompts or model fine-tuning. The key contribution is an identity-guided conditioning branch that enables anonymization even for subjects not well-represented in the model's training data, while also allowing for attribute-controllable anonymization. This is a significant advancement over existing methods that often lack control over facial attributes or require extensive training.
Key Takeaways
- •Introduces a 'reverse personalization' framework for face anonymization.
- •Enables direct image manipulation without text prompts or fine-tuning.
- •Uses an identity-guided conditioning branch for generalization.
- •Supports attribute-controllable anonymization.
- •Achieves a state-of-the-art balance between identity removal, attribute preservation, and image quality.
Reference
“The paper demonstrates a state-of-the-art balance between identity removal, attribute preservation, and image quality.”