30 citations · 77 across the 7 of their papers we have counts for
7 papers
Membership Inference Attacks on DNNs using Adversarial Perturbations
Hassan Ali, Adnan Qayyum, Ala Al-Fuqaha +1
Several membership inference (MI) attacks have been proposed to audit a target DNN. Given a set of subjects, MI attacks tell which subjects the target DNN has seen during training.…
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models
Hassan Ali, Muhammad Atif Butt, Fethi Filali +2
Recent works have shown that deep learning (DL) models can effectively learn city-wide crowd-flow patterns, which can be used for more effective urban planning and smart city manag…
Visual Heart Rate Estimation from RGB Facial Video using Spectral Reflectance
Bharath Ramakrishnan, Ruijia Deng, Hassan Ali
Estimation of the Heart rate from the facial video has a number of applications in the medical and fitness industries. Additionally, it has become useful in the field of gaming as…
HaS-Nets: A Heal and Select Mechanism to Defend DNNs Against Backdoor Attacks for Data Collection Scenarios
Hassan Ali, Surya Nepal, Salil S. Kanhere +1
We have witnessed the continuing arms race between backdoor attacks and the corresponding defense strategies on Deep Neural Networks (DNNs). Most state-of-the-art defenses rely on…
RED-Attack: Resource Efficient Decision based Attack for Machine Learning
Faiq Khalid, Hassan Ali, Muhammad Abdullah Hanif +3
Due to data dependency and model leakage properties, Deep Neural Networks (DNNs) exhibit several security vulnerabilities. Several security attacks exploited them but most of them…
SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters
Hassan Ali, Faiq Khalid, Hammad Tariq +4
In this paper, we introduce a novel technique based on the Secure Selective Convolutional (SSC) techniques in the training loop that increases the robustness of a given DNN by allo…
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks
Faiq Khalid, Hassan Ali, Hammad Tariq +4
Adversarial examples have emerged as a significant threat to machine learning algorithms, especially to the convolutional neural networks (CNNs). In this paper, we propose two quan…