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. The code is publicly available at: https://github.com/super-jw/LFADEA

A growing trend in the field of adversarial attacks is evolving from the digital domain to the more challenging physical domain. The previous works mainly employ printable adversarial patches with special textures in real-world physical attacks. However, due to lighting conditions and atmospheric scattering, the texture-based patches are prone to distortion in the long-range situation than in the close-range case, resulting in poor physical attack performance in remote sensing scenarios. Therefore, this article proposes a new physical attack method using single-color strip-based patches to hide the objects from being detected correctly in optical aerial detection. Specifically, we design a differentiable representation and an optimization method to optimize the position, thickness, and color of the adversarial strips. Compared with the traditional complex texture-based patch, the proposed strip-based patch is more robust when mapping from the digital domain to the physical domain. Extensive experiments are conducted on multiple datasets and real-world scenarios to evaluate the attack performance of various attack methods. The results show that the proposed strip-based adversarial patch has better attack performance against white-box, black-box, and even defense detectors. Furthermore, we can improve the physical attack success rate (ASR) in remote sensing scenarios by about 70% compared with previous texture-based methods.

Recommending new items in real-world e-commerce portals is a challenging problem as the cold start phenomenon, i.e., lacks of user-item interactions. To address this problem, we propose a novel recommendation model, i.e., adversarial neural network with multiple generators, to generate users from multiple perspectives of items' attributes. Namely, the generated users are represented by attribute-level features. As both users and items are attribute-level representations, we can implicitly obtain user-item attribute-level interaction information. In light of this, the new item can be recommended to users based on attribute-level similarity. Extensive experimental results on two item cold-start scenarios, movie and goods recommendation, verify the effectiveness of our proposed model as compared to state-of-the-art baselines.