4 citations · 5 across the 11 of their papers we have counts for
4 papers · 1 filter
Hierarchical Randomized Smoothing
Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski +1
Real-world data is complex and often consists of objects that can be decomposed into multiple entities (e.g. images into pixels, graphs into interconnected nodes). Randomized smoot…
Assessing Robustness via Score-Based Adversarial Image Generation
Marcel Kollovieh, Lukas Gosch, Marten Lienen +3
Most adversarial attacks and defenses focus on perturbations within small -norm constraints. However, threat models cannot capture all relevant semantics-preservin…
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
Francesco Campi, Lukas Gosch, Tom Wollschläger +2
We perform the first adversarial robustness study into Graph Neural Networks (GNNs) that are provably more powerful than traditional Message Passing Neural Networks (MPNNs). In par…
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
Yan Scholten, Jan Schuchardt, Simon Geisler +2
Randomized smoothing is one of the most promising frameworks for certifying the adversarial robustness of machine learning models, including Graph Neural Networks (GNNs). Yet, exis…