There has been rampant propagation of fake news and misinformation videos on many platforms lately, and moderation of such content faces many challenges that must be overcome. Recent research has shown the feasibility of identifying video titles from encrypted network traffic within a single platform, for example, within YouTube or Facebook. However, there are no existing methods for cross-platform video recognition, a crucial gap that this works aims to address. Encrypted video traffic classification within a single platform, that is, classifying the video title of a traffic trace of a video on one platform by training on traffic traces of videos on the same platform, has significant limitations due to the large number of video platforms available to users to upload harmful content to. To attempt to address this limitation, we conduct a feasibility analysis into and attempt to solve the challenge of recognizing videos across multiple platforms by using the traffic traces of videos on one platform only. We propose TripletViNet, a framework that encompasses i) platform-wise pre-processing, ii) an encoder trained utilizing triplet learning for improved accuracy and iii) multiclass classifier for classifying the video title of a traffic trace. To evaluate the performance of TripletViNet, a comprehensive dataset with traffic traces for 100 videos on six major platforms with the potential for spreading misinformation such as YouTube, X, Instagram, Facebook, Rumble, and Tumblr was collected and used to test TripletViNet in both closed-set and open-set scenarios. TripletViNet achieves significant improvements in accuracy due to the correlation between video traffic and the video's VBR, with impressive final accuracies exceeding 90% in certain scenarios.

翻译：近期，众多平台上虚假新闻与误导性视频的传播日益猖獗，对此类内容的审核面临诸多亟待克服的挑战。近期研究表明，在单一平台（例如YouTube或Facebook内部）通过加密网络流量识别视频标题具有可行性。然而，目前尚不存在跨平台视频识别的方法，这正是本文旨在解决的关键空白。单一平台内的加密视频流量分类（即通过在同一平台的视频流量轨迹上训练，来对该平台某视频流量轨迹的视频标题进行分类）存在显著局限性，因为用户可上传有害内容的视频平台数量庞大。为尝试解决这一局限，我们进行了可行性分析，并尝试仅利用单一平台的视频流量轨迹来解决跨多个平台识别视频的难题。我们提出了TripletViNet框架，该框架包含：i) 分平台预处理，ii) 利用三元组学习训练编码器以提升准确性，以及iii) 用于对流量轨迹视频标题进行分类的多类别分类器。为评估TripletViNet的性能，我们收集并构建了一个综合性数据集，包含YouTube、X、Instagram、Facebook、Rumble和Tumblr这六个主要平台上100个可能传播虚假信息的视频的流量轨迹，用于在闭集和开集场景下测试TripletViNet。得益于视频流量与视频可变比特率（VBR）之间的相关性，TripletViNet在准确率上取得了显著提升，在特定场景下最终准确率超过90%，表现令人印象深刻。