PrismWF: A Multi-Granularity Patch-Based Transformer for Robust Website Fingerprinting Attack

Computer Science > Cryptography and Security [Submitted on 22 Mar 2026] PrismWF: A Multi-Granularity Patch-Based Transformer for Robust Website Fingerprinting Attack Yuhao Pan, Wenchao Xu, Fushuo Huo, Haozhao Wang, Xiucheng Wang, Nan Cheng Tor is a low-latency anonymous communication network that protects user privacy by encrypting website traffic. However, recent website fingerprinting (WF) attacks have shown that encrypted traffic can still leak users' visited websites by exploiting statistical features such as packet size, direction, and inter-arrival time. Most existing WF attacks formulate the problem as a single-tab classification task, which significantly limits their effectiveness in realistic browsing scenarios where users access multiple websites concurrently, resulting in mixed traffic traces. To this end, we propose PrismWF, a multi-granularity patch-based Transformer for multi-tab WF attack. Specifically, we design a robust traffic feature representation for raw web traffic traces and extract multi-granularity features using convolutional kernels with different receptive fields. To effectively integrate information across temporal scales, the proposed model refines features through three hierarchical interaction mechanisms: inter-granularity detail supplementation from fine to coarse granularities, intra-granularity patch interaction with dedicated router tokens, and router-guided dual-level intra- and cross-granularity fusion. This design aligns with the cognitive logic of global coarse-grained reconnaissance and local fine-grained querying, enabling effective modeling of mixed traffic patterns in WF attack scenarios. Extensive experiments on various datasets and WF defenses demonstrate that our method achieves state-of-the-art performance compared to existing baselines. Comments: 14 pages, 7 figures Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.21117 [cs.CR]   (or arXiv:2603.21117v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.21117 Focus to learn more Submission history From: Yuhao Pan [view email] [v1] Sun, 22 Mar 2026 08:17:03 UTC (1,285 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)