2 citations · 2 across the 1 of their papers we have counts for
8 papers
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches
Reena Zelenkova, Jack Swallow, M. A. P. Chamikara +5
Biometric data, such as face images, are often associated with sensitive information (e.g medical, financial, personal government records). Hence, a data breach in a system storing…
Robust Training Using Natural Transformation
Shuo Wang, Lingjuan Lyu, Surya Nepal +3
Previous robustness approaches for deep learning models such as data augmentation techniques via data transformation or adversarial training cannot capture real-world variations th…
Defending Adversarial Attacks via Semantic Feature Manipulation
Shuo Wang, Tianle Chen, Surya Nepal +3
Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we propose a one-off and…
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning
Shuo Wang, Tianle Chen, Shangyu Chen +3
Anomaly detection aims to recognize samples with anomalous and unusual patterns with respect to a set of normal data. This is significant for numerous domain applications, such as…
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models
Shuo Wang, Surya Nepal, Carsten Rudolph +3
Transfer learning provides an effective solution for feasibly and fast customize accurate \textit{Student} models, by transferring the learned knowledge of pre-trained \textit{Teac…
A model for system developers to measure the privacy risk of data
Awanthika Senarath, Marthie Grobler, Nalin Asanka Gamagedara Arachchilage
In this paper, we propose a model that could be used by system developers to measure the privacy risk perceived by users when they disclose data into software systems. We first der…