GESA: Exploring Loss-based Adversarial Attacks in Volumetric Media Streaming

Volumetric point cloud media streaming has become increasingly popular in recent years. Streaming point clouds over the Internet requires efficient compression and transmission tech- niques to ensure that the client can receive the data in real-time. However, this poses several challenges in terms of bandwidth, latency, and quality of experience (QoE). The current network conditions are characterized by long delays and packet loss due to congestion, which results in the loss of many point clouds during transmission. As a result, clients may see incomplete or distorted objects, leading to low QoE. Researchers have proposed 3D point cloud completion models. These models aim to recover the missing points in an incomplete point cloud. However, all the proposed models in the literature consider incomplete objects from well-known synthetic datasets, which do not mimic packet loss. Therefore, these models fail to recover the missing points when deployed in real point cloud streaming scenarios. To fill this gap, we propose Gilbert-Elliott Shape Attack algorithm to generate a 3D point cloud incomplete dataset, namely GESA, that mimics the packet loss impact. This dataset is designed to be representative of real-world point cloud streaming scenarios, and it allows for the evaluation of the performance of existing completion models under realistic conditions. Our dataset is publicly available at https://in2gm.github.io/GESA/.