129 citations · 740 across the 61 of their papers we have counts for
11 papers · 1 filter
PatchBlock: A Lightweight Defense Against Adversarial Patches for Embedded EdgeAI Devices
Nandish Chattopadhyay, Abdul Basit, Amira Guesmi +3
Adversarial attacks pose a significant challenge to the reliable deployment of machine learning models in EdgeAI applications, such as autonomous driving and surveillance, which re…
DefensiveDR: Defending against Adversarial Patches using Dimensionality Reduction
Nandish Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif +2
Adversarial patch-based attacks have shown to be a major deterrent towards the reliable use of machine learning models. These attacks involve the strategic modification of localize…
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches
Nandish Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif +2
Adversarial attacks present a significant challenge to the dependable deployment of machine learning models, with patch-based attacks being particularly potent. These attacks intro…
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook
Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni +1
In this paper, we present a comprehensive survey of the current trends focusing specifically on physical adversarial attacks. We aim to provide a thorough understanding of the conc…
DAP: A Dynamic Adversarial Patch for Evading Person Detectors
Amira Guesmi, Ruitian Ding, Muhammad Abdullah Hanif +2
Patch-based adversarial attacks were proven to compromise the robustness and reliability of computer vision systems. However, their conspicuous and easily detectable nature challen…
PoisonedGNN: Backdoor Attack on Graph Neural Networks-based Hardware Security Systems
Lilas Alrahis, Satwik Patnaik, Muhammad Abdullah Hanif +2
Graph neural networks (GNNs) have shown great success in detecting intellectual property (IP) piracy and hardware Trojans (HTs). However, the machine learning community has demonst…
UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction
Lilas Alrahis, Satwik Patnaik, Muhammad Abdullah Hanif +2
Logic locking aims to prevent intellectual property (IP) piracy and unauthorized overproduction of integrated circuits (ICs). However, initial logic locking techniques were vulnera…
Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework
Muhammad Shafique, Alberto Marchisio, Rachmad Vidya Wicaksana Putra +1
The security and privacy concerns along with the amount of data that is required to be processed on regular basis has pushed processing to the edge of the computing systems. Deploy…
Exploiting Vulnerabilities in Deep Neural Networks: Adversarial and Fault-Injection Attacks
Faiq Khalid, Muhammad Abdullah Hanif, Muhammad Shafique
From tiny pacemaker chips to aircraft collision avoidance systems, the state-of-the-art Cyber-Physical Systems (CPS) have increasingly started to rely on Deep Neural Networks (DNNs…
GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking
Lilas Alrahis, Satwik Patnaik, Faiq Khalid +4
In this paper, we propose GNNUnlock, the first-of-its-kind oracle-less machine learning-based attack on provably secure logic locking that can identify any desired protection logic…
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…