8 papers
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…
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.…
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…
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…