no code implementations • 21 Jun 2022 • Shuiqiao Yang, Bao Gia Doan, Paul Montague, Olivier De Vel, Tamas Abraham, Seyit Camtepe, Damith C. Ranasinghe, Salil S. Kanhere
In this paper, we disclose the TRAP attack, a Transferable GRAPh backdoor attack.
no code implementations • 13 Oct 2020 • He Zhao, Thanh Nguyen, Trung Le, Paul Montague, Olivier De Vel, Tamas Abraham, Dinh Phung
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier.
no code implementations • 3 Oct 2019 • He Zhao, Trung Le, Paul Montague, Olivier De Vel, Tamas Abraham, Dinh Phung
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier.
no code implementations • ICLR 2019 • Tue Le, Tuan Nguyen, Trung Le, Dinh Phung, Paul Montague, Olivier De Vel, Lizhen Qu
Due to the sharp increase in the severity of the threat imposed by software vulnerabilities, the detection of vulnerabilities in binary code has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security.
no code implementations • 25 Feb 2019 • Yi Han, David Hubczenko, Paul Montague, Olivier De Vel, Tamas Abraham, Benjamin I. P. Rubinstein, Christopher Leckie, Tansu Alpcan, Sarah Erfani
Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting.
no code implementations • 17 Aug 2018 • Yi Han, Benjamin I. P. Rubinstein, Tamas Abraham, Tansu Alpcan, Olivier De Vel, Sarah Erfani, David Hubczenko, Christopher Leckie, Paul Montague
Despite the successful application of machine learning (ML) in a wide range of domains, adaptability---the very property that makes machine learning desirable---can be exploited by adversaries to contaminate training and evade classification.