14 citations · 31 across the 17 of their papers we have counts for
13 papers · 1 filter
Extracting Forward Invariant Sets from Neural Network-Based Control Barrier Functions
Goli Vaisi, James Ferlez, Yasser Shoukry
Training Neural Networks (NNs) to serve as Barrier Functions (BFs) is a popular way to improve the safety of autonomous dynamical systems. Despite significant practical success, th…
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound Propagation
Haitham Khedr, Yasser Shoukry
Formal certification of Neural Networks (NNs) is crucial for ensuring their safety, fairness, and robustness. Unfortunately, on the one hand, sound and complete certification algor…
Model Extraction Attacks Against Reinforcement Learning Based Controllers
Momina Sajid, Yanning Shen, Yasser Shoukry
We introduce the problem of model-extraction attacks in cyber-physical systems in which an attacker attempts to estimate (or extract) the feedback controller of the system. Extract…
BERN-NN: Tight Bound Propagation For Neural Networks Using Bernstein Polynomial Interval Arithmetic
Wael Fatnassi, Haitham Khedr, Valen Yamamoto +1
In this paper, we present BERN-NN as an efficient tool to perform bound propagation of Neural Networks (NNs). Bound propagation is a critical step in wide range of NN model checker…
CertiFair: A Framework for Certified Global Fairness of Neural Networks
Haitham Khedr, Yasser Shoukry
We consider the problem of whether a Neural Network (NN) model satisfies global individual fairness. Individual Fairness suggests that similar individuals with respect to a certain…
NNLander-VeriF: A Neural Network Formal Verification Framework for Vision-Based Autonomous Aircraft Landing
Ulices Santa Cruz, Yasser Shoukry
In this paper, we consider the problem of formally verifying a Neural Network (NN) based autonomous landing system. In such a system, a NN controller processes images from a camera…