Search Results for author:
Found 4 papers, 3 papers with code
Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks
This can be time-consuming and resource-intensive which hinders the deployment of these defenses in real-world applications.
Careful What You Wish For: on the Extraction of Adversarially Trained Models
In fact, a malicious user using only a query access to the prediction output of a model can extract it and obtain a high-accuracy and high-fidelity surrogate model.
Vehicle Detection and Tracking From Surveillance Cameras in Urban Scenes
Detecting and tracking vehicles in urban scenes is a crucial step in many traffic-related applications as it helps to improve road user safety among other benefits.
On the benchmarking of partitioned real-time systems
In this document, we present SFPBench, a generic benchmark framework for the assessment of performance metrics on partitioned RTSs.
Software Engineering Performance C.4
