Visual-Friendly Concept Protection via Selective Adversarial Perturbations

Xiaoyue Mi, Fan Tang, You Wu, Juan Cao, Peng Li, Yang Liu

Abstract:

Personalized concept generation by tuning diffusion models with a few images raises potential legal and ethical concerns regarding privacy and intellectual property rights.
Researchers attempt to prevent malicious personalization using adversarial perturbations.
However, previous efforts have mainly focused on the effectiveness of protection while neglecting the visibility of perturbations.
They utilize global adversarial perturbations, which introduce noticeable alterations to original images and significantly degrade visual quality.
In this work, we propose the \underline{V}isual-Friendly \underline{C}oncept \underline{Pro}tection (VCPro) framework, which prioritizes the protection of key concepts chosen by the image owner through adversarial perturbations with lower perceptibility.
To ensure these perturbations are as inconspicuous as possible, we introduce a relaxed optimization objective to identify the least perceptible yet effective adversarial perturbations, solved using the Lagrangian multiplier method.
Qualitative and quantitative experiments validate that VCPro achieves a better trade-off between the visibility of perturbations and protection effectiveness, effectively prioritizing the protection of target concepts in images with less perceptible perturbations.