1 citations · 1 across the 2 of their papers we have counts for
3 papers
Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures
Sayanton V. Dibbo, Adam Breuer, Juston Moore +1
Recent model inversion attack algorithms permit adversaries to reconstruct a neural network's private and potentially sensitive training data by repeatedly querying the network. In…
How Robust Are Energy-Based Models Trained With Equilibrium Propagation?
Siddharth Mansingh, Michal Kucer, Garrett Kenyon +2
Deep neural networks (DNNs) are easily fooled by adversarial perturbations that are imperceptible to humans. Adversarial training, a process where adversarial examples are added to…
Bound Tightening using Rolling-Horizon Decomposition for Neural Network Verification
Haoruo Zhao, Hassan Hijazi, Haydn Jones +3
Neural network verification aims at providing formal guarantees on the output of trained neural networks, to ensure their robustness against adversarial examples and enable their d…