Computation Cost Attack on 3D Gaussian Splatting

Jiahao Lu, Yifan Zhang, Qiuhong Shen, Xinchao Wang, Shuicheng Yan

Skywork AI, National University of Singapore

🔖[Arxiv]

Table of Contents

• Introduction
• Installation
  • Project Organization
  • Enviroment Installation
  • Download Datasets
  • Verify Installation
• Quickstart
  • Benchmark clean datasets computation cost
  • Poison, and benchmark poisoned datasets computation cost
  • Benchmark black-box attack performance
  • Defense strategy
• Visualization
• Citation

Introduction

We identify an overlooked security vulnerability of 3D Gaussian Splatting by revealing a novel attack surface: the computation complexity of training 3DGS could be maliciously manipulated by poisoning the input data.

The key observation behind this new attack surface lies in the inherent flexibility in model complexity.

Unlike NeRF or any other neural-network-driven machine learning systems which has pre-fixed and consistent computation cost (Fig. a), 3DGS has adaptively flexible computation complexity(i.e, number of parameters, training time cost and GPU memory consumption) depending on the content of input (Fig. b). This flexibility leaves backdoor to computation cost attacks (Fig. c).

We model attack via max-min bi-level optimization problem:

$$\mathcal{D}_p = \mathop{\arg\max}\limits_{\mathcal{D}_p} \mathcal{C}(\mathcal{G}^*), ~~ \text{s.t.\quad } \mathcal{G}^* = \mathop{\arg\min}\limits_{\mathcal{G}} \mathcal{L}(\mathcal{D}_p),$$

Where inner loop is the victim goal: minimize reconstruction error $\mathcal{L}(\mathcal{D}_p)$ during 3DGS training, and the outer loop is the attacker goal: making a poisoned dataset $\mathcal{D}_p$ to maximize victim's computation cost $\mathcal{C}(\mathcal{G}^*)$ spent on 3DGS training.

With constraints on perturbation strength ($\epsilon= 16/255$), for some scenes the attacker can raise the parameters needed to above 6 times and the GPU memory comsumption to above 8 times.

If the attacker is allowed to unlimitedly alter input data, the attack can be more damaging, sometimes achieving 80 GB GPU memory consumption, which is enough to cause an out-of-memory error and denial-of-service on most GPUs.

Installation

Project Organization

poison-splat
    |---assets
        (directory for project introduction figures)
    |---attacker
        (directory for attacker behavior)
    |---dataset
        (directory for saving clean and poisoned datasets)
    |---exp
        (directory for experiment scripts)
    |---log
        (directory for experiment records)
    |---victim
        (directory for victim behavior)

Enviroment Installation

First create a conda environment with pytorch-gpu. CUDA version 11.8 recommended.

conda create -n poison_splat python=3.11 -y
conda activate poison_splat
conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia -y
pip install -r requirements.txt

Download Datasets

Instructions of how to download NeRF-Synthetic, MIP-NeRF360 and Tanks-and-Temples original datasets are provided in dataset directory.

For poisoned datasets, you can run poison-splat to make a poisoned dataset following the data poisoning scripts or alternatively, downloading our poisoned datasets:

Google Drive: https://drive.google.com/file/d/11EZwsxRxWEAOnOThoOEJVre77Q5_SQfx/view?usp=sharing

Verify Installation

After installing the environment and downloading the NeRF-Synthetic dataset, you can verify your installation by running testing script:

bash exp/00_test/test_install.sh

Quickstart

Benchmark clean datasets computation cost

bash exp/01_main_exp/benchmark_nerf_synthetic_clean.sh
bash exp/01_main_exp/benchmark_mip_nerf_360_clean.sh
bash exp/01_main_exp/benchmark_tanks_and_temples_clean.sh

Please note that the above scripts assume you have 8-GPU environment. If not, please change the GPU device id by resetting the --gpu argument in each script.

Create poisoned datasets and benchmark corresponding computation cost

Constrained attack with perturbation 16/255:

bash exp/01_main_exp/eps16_attack_nerf_synthetic.sh
bash exp/01_main_exp/eps16_attack_mip_nerf_360.sh
bash exp/01_main_exp/eps16_attack_tanks_and_temples_1.sh
bash exp/01_main_exp/eps16_attack_tanks_and_temples_2.sh
bash exp/01_main_exp/eps16_attack_tanks_and_temples_3.sh

Unconstrained attack:

bash exp/01_main_exp/unbounded_attack_nerf_synthetic.sh
bash exp/01_main_exp/unbounded_attack_mip_nerf_360.sh
bash exp/01_main_exp/unbounded_attack_tanks_and_temples_1.sh
bash exp/01_main_exp/unbounded_attack_tanks_and_temples_2.sh
bash exp/01_main_exp/unbounded_attack_tanks_and_temples_3.sh

Benchmark black-box attack performance

We borrowed implementation from Scaffold-GS as a black-box victim, and benchmark their performance directly on the poisoned datasets for traditional Gaussian Splatting.

Following scripts in exp/02_blackbox_generalize/ to benchmark black-box attack performance.

To test the black-box performance for other variants of Gaussian Splatting, first implement the victim behavior in victim/ folder. Be especially careful about the environment conflict - for example the diff-gaussian-rasterization library. vicim/Scaffold-GS/submodules/diff-guassian-rasterization_scaffold/ gives an example of resolving such name conflicts.

Following the benchmark scripts victim/gaussian-splatting/benchmark.py and victim/Scaffold-GS/benchmark.py, write a script for benchmarking the newly added victim.

Defense strategy

We implemented one naive defense strategy in victim/gaussian-splatting/defense/ where the maximum number of Gaussians involved in training is restricted. Run scripts in exp/05_naive_defense/ to apply defensive training strategy.

Visualizations

We put some visualizations of attacker poisoned data and the corresponding victim reconstructions here.

Constrained attack ($\epsilon=16/255$)

dataset setting	Attacker Poisoned Image	Victim Reconstructed Image	PSNR
NS-Chair-eps16			37.07 dB
NS-Drums-eps16			30.32 dB
MIP-bicycle-eps16			18.20 dB
MIP-bonsai-eps16			22.67 dB

Unconstrained attack

dataset setting	Attacker Poisoned Image	Victim Reconstructed Image	PSNR
NS-Chair-Unconstrained			19.54 dB
NS-Drums-Unconstrained			18.65 dB
MIP-bicycle-Unconstrained			20.57 dB
MIP-bonsai-Unconstrained			23.66 dB

Citation

If you consider this repostory useful for your research, please consider citing:

@article{lu2024poisonsplat,
  title={Poison-splat: Computation Cost Attack on 3D Gaussian Splatting},
  author={Lu, Jiahao and Zhang, Yifan and Shen, Qiuhong and Wang, Xinchao and Yan, Shuicheng},
  journal={arXiv preprint arXiv:2410.08190},
  year={2024}
}