44 citations · 256 across the 65 of their papers we have counts for
11 papers · 1 filter
Split Learning without Local Weight Sharing to Enhance Client-side Data Privacy
Ngoc Duy Pham, Tran Khoa Phan, Alsharif Abuadbba +3
Split learning (SL) aims to protect user data privacy by distributing deep models between client-server and keeping private data locally. In SL training with multiple clients, the…
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks
Seonhye Park, Alsharif Abuadbba, Shuo Wang +4
Training deep neural networks (DNNs) requires large datasets and powerful computing resources, which has led some owners to restrict redistribution without permission. Watermarking…
Reconstruction Attack on Differential Private Trajectory Protection Mechanisms
Erik Buchholz, Alsharif Abuadbba, Shuo Wang +2
Location trajectories collected by smartphones and other devices represent a valuable data source for applications such as location-based services. Likewise, trajectories have the…
PhishClone: Measuring the Efficacy of Cloning Evasion Attacks
Arthur Wong, Alsharif Abuadbba, Mahathir Almashor +1
Web-based phishing accounts for over 90% of data breaches, and most web-browsers and security vendors rely on machine-learning (ML) models as mitigation. Despite this, links posted…
TransCAB: Transferable Clean-Annotation Backdoor to Object Detection with Natural Trigger in Real-World
Hua Ma, Yinshan Li, Yansong Gao +6
Object detection is the foundation of various critical computer-vision tasks such as segmentation, object tracking, and event detection. To train an object detector with satisfacto…
Profiler: Profile-Based Model to Detect Phishing Emails
Mariya Shmalko, Alsharif Abuadbba, Raj Gaire +3
Email phishing has become more prevalent and grows more sophisticated over time. To combat this rise, many machine learning (ML) algorithms for detecting phishing emails have been…
Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction
Ngoc Duy Pham, Alsharif Abuadbba, Yansong Gao +2
Split learning (SL) enables data privacy preservation by allowing clients to collaboratively train a deep learning model with the server without sharing raw data. However, SL still…
Towards A Critical Evaluation of Robustness for Deep Learning Backdoor Countermeasures
Huming Qiu, Hua Ma, Zhi Zhang +4
Since Deep Learning (DL) backdoor attacks have been revealed as one of the most insidious adversarial attacks, a number of countermeasures have been developed with certain assumpti…
Towards Web Phishing Detection Limitations and Mitigation
Alsharif Abuadbba, Shuo Wang, Mahathir Almashor +4
Web phishing remains a serious cyber threat responsible for most data breaches. Machine Learning (ML)-based anti-phishing detectors are seen as an effective countermeasure, and are…
Email Summarization to Assist Users in Phishing Identification
Amir Kashapov, Tingmin Wu, Alsharif Abuadbba +1
Cyber-phishing attacks recently became more precise, targeted, and tailored by training data to activate only in the presence of specific information or cues. They are adaptable to…
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World
Hua Ma, Yinshan Li, Yansong Gao +7
Deep learning models have been shown to be vulnerable to recent backdoor attacks. A backdoored model behaves normally for inputs containing no attacker-secretly-chosen trigger and…