# Targeted Adversarial Attacks on Generalizable Neural Radiance Fields

**Authors:** Andras Horvath, Csaba M. Jozsa  
**arXiv:** [2310.03578](https://arxiv.org/abs/2310.03578)  
**Date:** 2023-10-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2310.03578v1/

## Key points

- Neural Radiance Fields (NeRFs) are powerful for 3D scene representation and rendering
- NeRFs can generate high-quality images from sparse 2D observations
- Concerns raised about vulnerability of NeRFs to adversarial attacks in critical applications
- Paper explores susceptibility of generalizable NeRFs to different types of adversarial attacks
- Investigates low-intensity attacks and robust adversarial patches for real-world applications
- Successful targeted attacks demonstrated, generating specific predefined output scenes
- Attack intensity impacts NeRFs' performance, lower ℓ2 distance signifies more successful attack
- Majority of source views need to be attacked for effective attacks
- Patch-based attacks modify real environment itself to target neural networks
- Significance of low-intensity attacks diminishes in real-world applications due to limited access attackers have to image processing pipelines
- Targeted attacks aim at creating realistic scenes with unreal objects in rendered images
- Study emphasizes vulnerability of NeRFs to targeted attacks on rendered images
- Highlights the need for robust defense mechanisms to safeguard critical applications using NeRF technology

## Summary

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Neural Radiance Fields (NeRFs) have gained significant attention as a powerful tool for 3D scene representation and rendering. These data-driven models can generate high-quality images from sparse 2D observations, enabling realistic and interactive scene reconstructions. However, the increasing use of NeRFs in critical applications like augmented reality, robotics, and virtual environments raises concerns about their vulnerability to adversarial attacks. In this paper titled "Targeted Adversarial Attacks on Generalizable Neural Radiance Fields," the authors explore the susceptibility of generalizable NeRFs to different types of adversarial attacks. They investigate both low-intensity attacks and adversarial patches that could potentially be robust enough for real-world applications. The researchers also demonstrate targeted attacks, where they successfully generate specific predefined output scenes using these attack methods. The study presents intriguing findings regarding the impact of attack intensity on NeRFs' performance. Figure 3 illustrates how the quality of an attack is influenced by the number of source views and attacked samples in GeNeRFs (Generalizable Neural Radiance Fields). The results show that lower values on the Y-axis, indicating a smaller average ℓ2 distance between ground truth images and network-generated images after an attack, signify a more successful attack. Interestingly, attacks are not effective until the majority of source views are attacked. Furthermore, the paper discusses patch-based attacks as a practical approach to target neural networks by modifying the real environment itself. While low-intensity attacks may be academically interesting, their significance diminishes when considering real-world applications due to limited access attackers have to image processing pipelines. The research also highlights targeted attacks aimed at creating realistic scenes with unreal objects in rendered images. Although this study specifically focuses on attacking rendered images rather than investigating generated depth maps, it emphasizes the vulnerability of NeRFs to targeted attacks. Overall, this paper sheds light on potential threats posed by adversarial attacks to generalizable NeRFs. The findings underscore the need for robust defense mechanisms to safeguard critical applications utilizing NeRF technology in augmented reality, robotics, and virtual environments.