Privacy Protection Against Personalized Text-to-Image Synthesis via Cross-image Consistency Constraints

Recent advancements in diffusion models and personalization techniques have raised significant privacy concerns, as they enable the recreation of individual portraits from a limited number of publicly available images. This capability poses risks of realistic impersonations, prompting the need for effective privacy protection strategies.

The introduction of anti-personalization methods, which involve adding adversarial perturbations to disrupt personalization model training, highlights the urgency of addressing these privacy threats. However, existing methods often fail to utilize the multi-image nature of personalization, limiting their effectiveness.

This development underscores a growing discourse on data privacy and security in AI, particularly as related studies reveal challenges such as data retention in diffusion models and the potential for backdoor attacks. The need for comprehensive solutions that leverage inter-image relationships for enhanced privacy protection is becoming increasingly critical.