We are thrilled to announce that our conference paper entitled “Cross-Lingual Vulnerabilities of Text-to-Image Models: Evaluating Data Poisoning Attacks Across Ten Languages” has been accepted for publication in the 28th International Conference on Pattern Recognition (ICPR 2026). Congraturations Kakebayashi-kun!
Ryohei Kakebayashi and Tatsuya Mori, "Cross-Lingual Vulnerabilities of Text-to-Image Models: Evaluating Data Poisoning Attacks Across Ten Languages." In Proc. of the 28th International Conference on Pattern Recognition (ICPR 2026) Lyon, France, Sep 2026. Published in Springer LNCS.
We are thrilled to announce that our conference paper entitled “TEV-IDS: CAN Intrusion Detection System via Spatial Temporal-Entropy-Variation Fingerprinting” has been accepted for publication in IEEE 103rd Vehicular Technology Conference (VTC2026-Spring). Congratulations to Peng-kun and kudos to the entire team!
In this work, we propose TEV-IDS, an ID-agnostic intrusion detection framework for in-vehicle CAN networks. Existing systems are largely limited to binary benign/attack decisions and depend on vehicle-specific CAN ID semantics, which hinders both fine-grained attack diagnosis and scalable deployment across heterogeneous platforms. TEV-IDS overcomes these challenges by constructing a Temporal–Entropy–Variation (TEV) feature space from ID-independent cues (inter-arrival intervals, payload entropy, and bit-level variation), and projecting them via a Tri-Perspective View (TPV) into compact 2D feature maps for a lightweight classifier. A stratified few-shot adaptation protocol further enables calibration to unseen vehicles with minimal target data. On the CAN-MIRGU dataset, TEV-IDS achieves a macro-F1 above 0.99 for 17-class attack classification, while cross-domain evaluation on the ROAD dataset yields a macro-AUC above 0.924 with limited calibration data, all under real-time CPU inference latency.
Shuo Peng, Zhihe Zhang, Go Tsuruoka, and Tatsuya Mori. "TEV-IDS: CAN Intrusion Detection System via Spatial Temporal-Entropy-Variation Fingerprinting." In Proc. of IEEE 103rd Vehicular Technology Conference (VTC2026-Spring), Nice, France, Jun 2026.
