no code implementations • 8 Apr 2024 • Viet Quoc Vo, Ehsan Abbasnejad, Damith C. Ranasinghe
We study the unique, less-well understood problem of generating sparse adversarial samples simply by observing the score-based replies to model queries.
1 code implementation • 5 Dec 2022 • Bao Gia Doan, Ehsan Abbasnejad, Javen Qinfeng Shi, Damith C. Ranasinghe
We recognize the adversarial learning approach for approximating the multi-modal posterior distribution of a Bayesian model can lead to mode collapse; consequently, the model's achievements in robustness and performance are sub-optimal.
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.
1 code implementation • 31 Jan 2022 • Viet Quoc Vo, Ehsan Abbasnejad, Damith C. Ranasinghe
The ability to extract information from solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world systems, such as autonomous cars or machine learning models exposed as a service (MLaaS).
1 code implementation • 10 Dec 2021 • Viet Quoc Vo, Ehsan Abbasnejad, Damith C. Ranasinghe
In our study, we first deep dive into recent state-of-the-art decision-based attacks in ICLR and SP to highlight the costly nature of discovering low distortion adversarial employing gradient estimation methods.
no code implementations • 19 Nov 2021 • Bao Gia Doan, Minhui Xue, Shiqing Ma, Ehsan Abbasnejad, Damith C. Ranasinghe
Now, an adversary can arm themselves with a patch that is naturalistic, less malicious-looking, physically realizable, highly effective achieving high attack success rates, and universal.
no code implementations • 12 Oct 2021 • Alireza Abedin, Hamid Rezatofighi, Damith C. Ranasinghe
Human activity recognition (HAR) is an important research field in ubiquitous computing where the acquisition of large-scale labeled sensor data is tedious, labor-intensive and time consuming.
Generative Adversarial Network Human Activity Recognition +1
no code implementations • 14 Jul 2020 • Alireza Abedin, Mahsa Ehsanpour, Qinfeng Shi, Hamid Rezatofighi, Damith C. Ranasinghe
Wearables are fundamental to improving our understanding of human activities, especially for an increasing number of healthcare applications from rehabilitation to fine-grained gait analysis.
1 code implementation • 19 Mar 2020 • Michael Chesser, Asangi Jayatilaka, Renuka Visvanathan, Christophe Fumeaux, Alanson Sample, Damith C. Ranasinghe
The sensor design allows deriving ultra low resolution acceleration data from the rate of change of unique RFID tag identifiers in accordance with the movement of a patient's upper body.
3 code implementations • 23 Nov 2019 • Yansong Gao, Yeonjae Kim, Bao Gia Doan, Zhi Zhang, Gongxuan Zhang, Surya Nepal, Damith C. Ranasinghe, Hyoungshick Kim
In particular, for vision tasks, we can always achieve a 0% FRR and FAR.
Cryptography and Security
1 code implementation • 9 Aug 2019 • Bao Gia Doan, Ehsan Abbasnejad, Damith C. Ranasinghe
Notably, in contrast to existing approaches, our approach removes the need for ground-truth labelled data or anomaly detection methods for Trojan detection or retraining a model or prior knowledge of an attack.
Cryptography and Security
no code implementations • 6 Jun 2019 • Alireza Abedin, S. Hamid Rezatofighi, Qinfeng Shi, Damith C. Ranasinghe
Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people.
4 code implementations • 18 Feb 2019 • Yansong Gao, Chang Xu, Derui Wang, Shiping Chen, Damith C. Ranasinghe, Surya Nepal
Since the trojan trigger is a secret guarded and exploited by the attacker, detecting such trojan inputs is a challenge, especially at run-time when models are in active operation.
Cryptography and Security
1 code implementation • 1 Dec 2018 • Hoa Van Nguyen, Hamid Rezatofighi, David Taggart, Bertram Ostendorf, Damith C. Ranasinghe
We investigate the problem of tracking and planning for a UAV in a task to locate multiple radio-tagged wildlife in a three-dimensional (3D) setting in the context of our TrackerBots research project.
no code implementations • 19 May 2018 • Yansong Gao, Yang Su, Lei Xu, Damith C. Ranasinghe
A Physical unclonable functions (PUF), alike a fingerprint, exploits manufacturing randomness to endow each physical item with a unique identifier.
Cryptography and Security
1 code implementation • 5 Dec 2017 • Hoa Van Nguyen, Michael Chesser, Fei Chen, S. Hamid Rezatofighi, Damith C. Ranasinghe
Autonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods.
Systems and Control Robotics
no code implementations • 20 Jun 2017 • Yansong Gao, Said F. Al-Sarawi, Derek Abbott, Ahmad-Reza Sadeghi, Damith C. Ranasinghe
Physical unclonable functions (PUFs), as hardware security primitives, exploit manufacturing randomness to extract hardware instance-specific secrets.
Cryptography and Security
no code implementations • 11 Mar 2016 • Roberto L. Shinmoto Torres, Damith C. Ranasinghe, Qinfeng Shi, Anton Van Den Hengel
The present study introduces a method for improving the classification performance of imbalanced multiclass data streams from wireless body worn sensors.