SiMLA'26: The 8th International Workshop on Security in Machine Learning and its Applications
About us
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.
Important Dates
Paper Submission
Research Paper Submission Tracks
Call for Papers Description
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.
Committee
Program Committee
Yanjun Zhang
University of Technology Sydney, Australia
Zirui Gong
Griffith University, Australia
Dayong Ye
City University of Macau, Macau
Yufei Chen
City University of Hong Kong, Hong Kong
Shuren Qi
The Chinese University of Hong Kong, Hong Kong
Maggie Liu
RMIT University, Australia
Daniël Reijsbergen
Singapore University of Technology and Design, Singapore
Yuantian Miao
The University of Newcastle, Australia
Zhongyun Hua
Harbin Institute of Technology, China
Yushu Zhang
Jiangxi University of Finance and Economics, China
Chuan Qin
University of Shanghai for Science and Technology, China
Jiaojiao Jiang
University of New South Wales, Australia
Nan Wang
CSIRO, Australia
Shangqi Lai
University of Melbourne, Australia
Naipeng Dong
The University of Queensland, Australia
Viet Vo
Swinburne University of Technology, Australia
Yang Cao
Tokyo Institute of Technology, Japan
Yu Li
Zhejiang University, China
Yansong Gao
University of Western Australia, Australia
Xiaoyong Yuan
Clemson University, USA
Junxu Liu
The Hong Kong Polytechnic University, Hong Kong
Chenxi Qiu
University of North Texas, USA
Organizing Committee
Leo Zhang
leo.zhang@griffith.edu.au, Griffith University, Australia
Yifeng Zheng
yifeng.zheng@polyu.edu.hk, The Hong Kong Polytechnic University, Hong Kong
Fuyi Wang
fuyi.wang@rmit.edu.au, RMIT University, Australia