Latent Feature and Attention Dual Erasure Attack against Multi-View Diffusion Models for 3D Assets Protection

TL;DR

This paper introduces a novel attack method that disrupts multi-view diffusion models used for 3D asset reconstruction, effectively protecting intellectual property by targeting latent features and attention mechanisms.

Contribution

It is the first to address IP protection for MVDMs by proposing a dual erasure attack that considers multi-view and multi-domain consistency disruptions.

Findings

Achieves superior attack effectiveness on state-of-the-art MVDMs

Demonstrates high transferability of the attack across models

Maintains robustness against defense strategies

Abstract

Multi-View Diffusion Models (MVDMs) enable remarkable improvements in the field of 3D geometric reconstruction, but the issue regarding intellectual property has received increasing attention due to unauthorized imitation. Recently, some works have utilized adversarial attacks to protect copyright. However, all these works focus on single-image generation tasks which only need to consider the inner feature of images. Previous methods are inefficient in attacking MVDMs because they lack the consideration of disrupting the geometric and visual consistency among the generated multi-view images. This paper is the first to address the intellectual property infringement issue arising from MVDMs. Accordingly, we propose a novel latent feature and attention dual erasure attack to disrupt the distribution of latent feature and the consistency across the generated images from multi-view and multi-domain simultaneously. The experiments conducted on SOTA MVDMs indicate that our approach achieves superior performances in terms of attack effectiveness, transferability, and robustness against defense methods. Therefore, this paper provides an efficient solution to protect 3D assets from MVDMs-based 3D geometry reconstruction.