VVRec: Reconstruction Attacks on DL-based Volumetric Video Upstreaming via Latent Diffusion Model with Gamma Distribution

TL;DR

This paper introduces VVRec, a novel deep learning-based reconstruction attack on volumetric video streaming that effectively recovers high-quality point clouds, exposing privacy vulnerabilities in current compression methods.

Contribution

VVRec is the first attack scheme targeting DL-based volumetric video upstreaming, utilizing latent diffusion models with Gamma distribution for high-quality reconstruction.

Findings

Achieves 64.70dB reconstruction accuracy

Reduces distortion by 46.39% compared to baselines

Outperforms existing defenses in reconstruction quality

Abstract

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.