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09/05/2023
The Adversarial Implications of Variable-Time Inference
Machine learning (ML) models are known to be vulnerable to a number of a...
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06/14/2023
X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail
Object detection models, which are widely used in various domains (such ...
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03/13/2023
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images
Weird, unusual, and uncanny images pique the curiosity of observers beca...
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03/02/2023
IoT Device Identification Based on Network Communication Analysis Using Deep Learning
Attack vectors for adversaries have increased in organizations because o...
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12/05/2022
YolOOD: Utilizing Object Detection Concepts for Out-of-Distribution Detection
Out-of-distribution (OOD) detection has attracted a large amount of atte...
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11/27/2022
Latent SHAP: Toward Practical Human-Interpretable Explanations
Model agnostic feature attribution algorithms (such as SHAP and LIME) ar...
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11/24/2022
Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models
In recent years, various watermarking methods were suggested to detect c...
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11/16/2022
Attacking Object Detector Using A Universal Targeted Label-Switch Patch
Adversarial attacks against deep learning-based object detectors (ODs) h...
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08/23/2022
Transferability Ranking of Adversarial Examples
Adversarial examples can be used to maliciously and covertly change a mo...
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07/25/2022
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models
While vision-and-language models perform well on tasks such as visual qu...
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05/13/2022
EyeDAS: Securing Perception of Autonomous Cars Against the Stereoblindness Syndrome
The ability to detect whether an object is a 2D or 3D object is extremel...
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02/21/2022
bAdvertisement: Attacking Advanced Driver-Assistance Systems Using Print Advertisements
In this paper, we present bAdvertisement, a novel attack method against ...
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02/14/2022
AnoMili: Spoofing Prevention and Explainable Anomaly Detection for the 1553 Military Avionic Bus
MIL-STD-1553, a standard that defines a communication bus for interconne...
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01/21/2022
The Security of Deep Learning Defences for Medical Imaging
Deep learning has shown great promise in the domain of medical image ana...
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01/16/2022
Adversarial Machine Learning Threat Analysis in Open Radio Access Networks
The Open Radio Access Network (O-RAN) is a new, open, adaptive, and inte...
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01/16/2022
Evaluating the Security of Open Radio Access Networks
The Open Radio Access Network (O-RAN) is a promising RAN architecture, a...
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01/02/2022
VISAS – Detecting GPS spoofing attacks against drones by analyzing camera's video stream
In this study, we propose an innovative method for the real-time detecti...
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11/21/2021
Adversarial Mask: Real-World Adversarial Attack Against Face Recognition Models
Deep learning-based facial recognition (FR) models have demonstrated sta...
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10/24/2021
Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples
Few-shot classifiers have been shown to exhibit promising results in use...
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09/14/2021
Dodging Attack Using Carefully Crafted Natural Makeup
Deep learning face recognition models are used by state-of-the-art surve...
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07/05/2021
A Framework for Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems
Although cyberattacks on machine learning (ML) production systems can be...
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06/30/2021
The Threat of Offensive AI to Organizations
AI has provided us with the ability to automate tasks, extract informati...
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06/15/2021
CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an In-Vehicle CAN Bus Based on Deep Features of Voltage Signals
The Controller Area Network (CAN) is used for communication between in-v...
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06/13/2021
RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks
Radar systems are mainly used for tracking aircraft, missiles, satellite...
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05/02/2021
Who's Afraid of Adversarial Transferability?
Adversarial transferability, namely the ability of adversarial perturbat...
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02/10/2021
Enhancing Real-World Adversarial Patches with 3D Modeling Techniques
Although many studies have examined adversarial examples in the real wor...
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12/23/2020
BENN: Bias Estimation Using Deep Neural Network
The need to detect bias in machine learning (ML) models has led to the d...
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12/23/2020
The Translucent Patch: A Physical and Universal Attack on Object Detectors
Physical adversarial attacks against object detectors have seen increasi...
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12/09/2020
Detection of Adversarial Supports in Few-shot Classifiers Using Feature Preserving Autoencoders and Self-Similarity
Few-shot classifiers excel under limited training samples, making it use...
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11/11/2020
Deja vu from the SVM Era: Example-based Explanations with Outlier Detection
Understanding the features that contributed to a prediction is important...
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10/30/2020
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers
The performance of a machine learning-based malware classifier depends o...
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10/25/2020
Dynamic Adversarial Patch for Evading Object Detection Models
Recent research shows that neural networks models used for computer visi...
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10/24/2020
Stop Bugging Me! Evading Modern-Day Wiretapping Using Adversarial Perturbations
Mass surveillance systems for voice over IP (VoIP) conversations pose a ...
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10/19/2020
When Bots Take Over the Stock Market: Evasion Attacks Against Algorithmic Traders
In recent years, machine learning has become prevalent in numerous tasks...
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10/07/2020
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples
Recent work on adversarial learning has focused mainly on neural network...
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09/11/2020
Fairness Matters – A Data-Driven Framework Towards Fair and High Performing Facial Recognition Systems
Facial recognition technologies are widely used in governmental and indu...
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08/16/2020
GLOD: Gaussian Likelihood Out of Distribution Detector
Discriminative deep neural networks (DNNs) do well at classifying input ...
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08/10/2020
An Automated, End-to-End Framework for Modeling Attacks From Vulnerability Descriptions
Attack graphs are one of the main techniques used to automate the risk a...
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07/05/2020
Adversarial Learning in the Cyber Security Domain
In recent years, machine learning algorithms, and more specially, deep l...
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06/30/2020
Autosploit: A Fully Automated Framework for Evaluating the Exploitability of Security Vulnerabilities
The existence of a security vulnerability in a system does not necessari...
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06/18/2020
Lightweight Collaborative Anomaly Detection for the IoT using Blockchain
Due to their rapid growth and deployment, the Internet of things (IoT) h...
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03/07/2020
ATHAFI: Agile Threat Hunting And Forensic Investigation
Attackers rapidly change their attacks to evade detection. Even the most...
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03/05/2020
DANTE: A framework for mining and monitoring darknet traffic
Trillions of network packets are sent over the Internet to destinations ...
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02/25/2020
IoT Device Identification Using Deep Learning
The growing use of IoT devices in organizations has increased the number...
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02/17/2020
A New Methodology for Information Security Risk Assessment for Medical Devices and Its Evaluation
As technology advances towards more connected and digital environments, ...
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02/06/2020
Neural Network Representation Control: Gaussian Isolation Machines and CVC Regularization
In many cases, neural network classifiers are likely to be exposed to in...
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02/04/2020
BRIGHTNESS: Leaking Sensitive Data from Air-Gapped Workstations via Screen Brightness
Air-gapped computers are systems that are kept isolated from the Interne...
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12/08/2019
Exploring the Back Alleys: Analysing The Robustness of Alternative Neural Network Architectures against Adversarial Attacks
Recent discoveries in the field of adversarial machine learning have sho...
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10/23/2019
The Security of IP-based Video Surveillance Systems
IP-based Surveillance systems protect industrial facilities, railways, g...
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08/19/2019