The Security in Machine Learning and its Applications (SiMLA) workshop provides a dedicated forum to address the critical challenges of securing modern machine learning (ML) systems. With ML technologies increasingly deployed in domains such as autonomous driving, biometric authentication, and intelligent surveillance, their vulnerabilities to adversarial attacks, data poisoning, and privacy breaches raise significant concerns. SiMLA 2026 aims to bring together researchers and practitioners to exchange ideas, present novel techniques, and discuss emerging threats in trustworthy ML.
Submissions must not substantially duplicate work that any of the authors has published elsewhere or has submitted in parallel to any other venue with formally published proceedings. Submissions must be anonymous, with no author names, affiliations, acknowledgement or obvious references. Each submission must begin with a title, short abstract, and a list of keywords. The introduction should summarize the contributions of the paper at a level appropriate for a non-specialist reader. All submissions must follow the LNCS format with a page limit of 20 pages (incl. references). Work-in-progress papers are welcome.
University of Technology Sydney, Australia
Griffith University, Australia
City University of Macau, Macau
City University of Hong Kong, Hong Kong
The Chinese University of Hong Kong, Hong Kong
RMIT University, Australia
Singapore University of Technology and Design, Singapore
The University of Newcastle, Australia
Harbin Institute of Technology, China
Jiangxi University of Finance and Economics, China
University of Shanghai for Science and Technology, China
University of New South Wales, Australia
CSIRO, Australia
University of Melbourne, Australia
The University of Queensland, Australia
Swinburne University of Technology, Australia
Tokyo Institute of Technology, Japan
Zhejiang University, China
University of Western Australia, Australia
Clemson University, USA
The Hong Kong Polytechnic University, Hong Kong
University of North Texas, USA
leo.zhang@griffith.edu.au, Griffith University, Australia
yifeng.zheng@polyu.edu.hk, The Hong Kong Polytechnic University, Hong Kong
fuyi.wang@rmit.edu.au, RMIT University, Australia
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