7 papers
Dataset Usage Inference without Shadow Models or Held-out Data
Wojciech Åapacz, StanisÅaw Pawlak, Jan DubiÅski +2
How much of my data was used to train a machine learning model? Dataset Usage Inference (DUI) aims to answer this by estimating what fraction of a dataset contributed to a model's…
Privacy Attacks on Image AutoRegressive Models
Antoni Kowalczuk, Jan DubiÅski, Franziska Boenisch +1
Image AutoRegressive generation has emerged as a new powerful paradigm with image autoregressive models (IARs) matching state-of-the-art diffusion models (DMs) in image quality (FI…
Universal Properties of Activation Sparsity in Modern Large Language Models
Filip Szatkowski, Patryk BÄdkowski, Alessio Devoto +5
Activation sparsity is an intriguing property of deep neural networks that has been extensively studied in ReLU-based models, due to its advantages for efficiency, robustness, and…
On Stealing Graph Neural Network Models
Marcin Podhajski, Jan DubiÅski, Franziska Boenisch +3
Current graph neural network (GNN) model-stealing methods rely heavily on queries to the victim model, assuming no hard query limits. However, in reality, the number of allowed que…
Backdoor Vectors: a Task Arithmetic View on Backdoor Attacks and Defenses
StanisÅaw Pawlak, Jan DubiÅski, Daniel Marczak +1
Model merging (MM) recently emerged as an effective method for combining large deep learning models. However, it poses significant security risks. Recent research shows that it is…
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
Marcin Podhajski, Jan DubiÅski, Franziska Boenisch +3
Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures. Especially inductive GNNs, which allow for the processing…