Work

ARTMAN '25: Third Workshop on Recent Advances in Resilient and Trustworthy MAchine learning-driveN systems

Year: 2025

Type: conference-paper

Abstract: The ARTMAN workshop aims to bring together academic researchers and industry practitioners from diverse domains, primarily security & privacy and machine learning, but also various application fields, to collaboratively explore and discuss resilient and trustworthy machine learning-powered applications and systems. This workshop focuses on AI/ML application domains and welcomes contributions on both foundational and applied aspects of ML across various industries, including transportation, aerospace, healthcare, energy, and finance, among others, showcasing AI-driven advances in performance and efficiency. This workshop also seeks contributions on the application of reliable and secure AI/ML algorithms, especially knowledge-informed approaches, to improve resilience and trust, particularly in human-machine partnerships and interactions within such scenarios.

Source: ACM SIGSAC Conference on Computer and Communications Security (CCS)

DOI: 10.1145/3719027.3767660

Authors: Gregory Blanc, Takeshi Takahashi, Zonghua Zhang

Institutions: Institut Polytechnique de Paris, Télécom SudParis, National Institute of Information and Communications Technology

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Cognitive and programmable security for resilient next-generation networks