4 papers
Are Safety Guarantees in Neural Networks Safe? How to Compute Trustworthy Robustness Certifications
Merkouris Papamichail, Konstantinos Varsos, Giorgos Flouris +1
A primary challenge in AI safety is the existence of adversarial examples -- slightly distorted inputs that cause a neural network (NN) to misclassify. To mitigate this problem, re…
The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification
Merkouris Papamichail, Konstantinos Varsos, Giorgos Flouris +1
Many neural network (NN) verification systems represent the network's input-output relation as a constraint program. Sound and complete, representations involve integer constraints…
Interval Certifications for Multilayered Perceptrons via Lattice Traversal
Merkouris Papamichail, Konstantinos Varsos, Giorgos Flouris +1
In this work we present a rigorous theoretical framework to a foundational problem of AI safety, namely adversarial robustness. In particular, we show that the adversarial robustne…
Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
Ines Akaichi, Giorgos Flouris, Irini Fundulaki +1
In the digital age, data frequently crosses organizational and jurisdictional boundaries, making effective governance essential. Usage control policies have emerged as a key paradi…