1 citations · 1 across the 14 of their papers we have counts for
15 papers
ARMOR: Manifold-Oriented Training for Adversarially Robust Aerial Object Detection under Data Scarcity
Haoran Wang, Matthew Lau, Alec Helbling +7
Aerial object detection is increasingly deployed in real-world applications, but models remain vulnerable to physical, universal adversarial patches that cause them to miss objects…
WiFiSpectralJam: A Large-Scale Open Wi-Fi Spectral Scan Dataset with Controlled RF Jamming
Dania Herzalla, Govind Singh, Willian T. Lunardi +1
WiFiSpectralJam is a Wi-Fi spectral-scan dataset comprising 14.52 GB, 96,090 CSV files, and 522,771,130 ordered spectral observations using commodity Wi-Fi sensing hardware. Measur…
ALLUDE: A Unified Evaluation System for Configurable Attacks in Differentiable Environments
Mansi Phute, Alexander Greenhalgh, Matthew Hull +8
Adversarial attacks against vision models like object detectors are often evaluated under limited conditions, leaving their performance under-characterized. Bridging simulation and…
Learning Hyperspherical Time-Frequency Representations for Time-Series Out-of-Distribution Detection
Willian T. Lunardi, Samridha Shrestha, Martin Andreoni
Out-of-distribution (OOD) detection for time-series data remains comparatively underexplored compared to vision and language, with a limited principled understanding of how supervi…
Toward an Intrusion Detection System for a Virtualization Framework in Edge Computing
Everton de Matos, Hazaa Alameri, Willian Tessaro Lunardi +2
Edge computing pushes computation closer to data sources, but it also expands the attack surface on resource-constrained devices. This work explores the deployment of the Lightweig…
UNDREAM: Bridging Differentiable Rendering and Photorealistic Simulation for End-to-end Adversarial Attacks
Mansi Phute, Matthew Hull, Haoran Wang +6
Deep learning models deployed in safety critical applications like autonomous driving use simulations to test their robustness against adversarial attacks in realistic conditions.…
ComplicitSplat: Downstream Models are Vulnerable to Blackbox Attacks by 3D Gaussian Splat Camouflages
Matthew Hull, Haoyang Yang, Pratham Mehta +8
As 3D Gaussian Splatting (3DGS) gains rapid adoption in safety-critical tasks for efficient novel-view synthesis from static images, how might an adversary tamper images to cause h…
Graph Neural Networks for Jamming Source Localization
Dania Herzalla, Willian T. Lunardi, Martin Andreoni
Graph-based learning provides a powerful framework for modeling complex relational structures; however, its application within the domain of wireless security remains significantly…
3D Gaussian Splat Vulnerabilities
Matthew Hull, Haoyang Yang, Pratham Mehta +8
With 3D Gaussian Splatting (3DGS) being increasingly used in safety-critical applications, how can an adversary manipulate the scene to cause harm? We introduce CLOAK, the first at…
Secure Safety Filter: Towards Safe Flight Control under Sensor Attacks
Xiao Tan, Junior Sundar, Renzo Bruzzone +5
Modern autopilot systems are prone to sensor attacks that can jeopardize flight safety. To mitigate this risk, we proposed a modular solution: the secure safety filter, which exten…
Contrastive Representation Modeling for Anomaly Detection
Willian T. Lunardi, Abdulrahman Banabila, Dania Herzalla +1
Distance-based anomaly detection methods rely on compact in-distribution (ID) embeddings that are well separated from anomalies. However, conventional contrastive learning strategi…
RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering
Matthew Hull, Haoran Wang, Matthew Lau +8
Differentiable rendering techniques like Gaussian Splatting and Neural Radiance Fields have become powerful tools for generating high-fidelity models of 3D objects and scenes. Thei…
Non-Robust Features are Not Always Useful in One-Class Classification
Matthew Lau, Haoran Wang, Alec Helbling +5
The robustness of machine learning models has been questioned by the existence of adversarial examples. We examine the threat of adversarial examples in practical applications that…
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection
Dania Herzalla, Willian T. Lunardi, Martin Andreoni Lopez
The effectiveness of network intrusion detection systems, predominantly based on machine learning, are highly influenced by the dataset they are trained on. Ensuring an accurate re…
Benchmarking and Security Considerations of Wi-Fi FTM for Ranging in IoT Devices
Govind Singh, Anshul Pandey, Monika Prakash +2
The IEEE 802.11mc standard introduces fine time measurement (Wi-Fi FTM), allowing high-precision synchronization between peers and round-trip time calculation (Wi-Fi RTT) for locat…