首个针对点云视频压缩的重建攻击方法，可高精度还原原始数据
VVRec: Reconstruction Attacks on DL-based Volumetric Video Upstreaming via Latent Diffusion Model with Gamma Distribution
- 基于伽马分布的潜在扩散模型与四模块神经网络
- 重建质量达64.70dB，失真降低46.39%
- 适用于研究隐私安全的开发者与安全评估人员
随着三维体视频应用（如自动驾驶、虚拟现实、混合现实）的普及，开发者开始采用深度学习技术对体视频帧（点云）进行压缩以实现高效上传。相较于传统方案（如MPEG、JPEG），现有深度学习方法在效率、失真和硬件支持方面更具优势。然而，隐私风险随之而来，尤其是针对中间传输结果的重建攻击。本文提出VVRec，据我们所知是首个面向深度学习体视频压缩的重建攻击方案。该方法通过四个精心设计的神经网络模块，利用最新的带有伽马分布的潜在扩散模型及优化算法，能够从截获的中间结果中重建出高质量点云。在三个体视频数据集上的实验表明，VVRec实现了64.70dB的重建准确率，相比基线方法失真降低46.39%。
原文摘要 · Abstract (English)
With the popularity of 3D volumetric video applications, such as Autonomous Driving, Virtual Reality, and Mixed Reality, current developers have turned to deep learning for compressing volumetric video frames, i.e., point clouds for video upstreaming. The latest deep learning-based solutions offer higher efficiency, lower distortion, and better hardware support compared to traditional ones like MPEG and JPEG. However, privacy threats arise, especially reconstruction attacks targeting to recover the original input point cloud from the intermediate results. In this paper, we design VVRec, to the best of our knowledge, which is the first targeting DL-based Volumetric Video Reconstruction attack scheme. VVRec demonstrates the ability to reconstruct high-quality point clouds from intercepted transmission intermediate results using four well-trained neural network modules we design. Leveraging the latest latent diffusion models with Gamma distribution and a refinement algorithm, VVRec excels in reconstruction quality, color recovery, and surpasses existing defenses. We evaluate VVRec using three volumetric video datasets. The results demonstrate that VVRec achieves 64.70dB reconstruction accuracy, with an impressive 46.39% reduction of distortion over baselines.
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