22 citations · 67 across the 35 of their papers we have counts for
6 papers · 1 filter
PEPSI: Practically Efficient Private Set Intersection in the Unbalanced Setting
Rasoul Akhavan Mahdavi, Nils Lukas, Faezeh Ebrahimianghazani +7
Two parties with private data sets can find shared elements using a Private Set Intersection (PSI) protocol without revealing any information beyond the intersection. Circuit PSI p…
Leveraging Optimization for Adaptive Attacks on Image Watermarks
Nils Lukas, Abdulrahman Diaa, Lucas Fenaux +1
Untrustworthy users can misuse image generators to synthesize high-quality deepfakes and engage in unethical activities. Watermarking deters misuse by marking generated content wit…
Pick your Poison: Undetectability versus Robustness in Data Poisoning Attacks
Nils Lukas, Florian Kerschbaum
Deep image classification models trained on vast amounts of web-scraped data are susceptible to data poisoning - a mechanism for backdooring models. A small number of poisoned samp…
Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
Abdulrahman Diaa, Lucas Fenaux, Thomas Humphries +9
Machine Learning as a Service (MLaaS) is an increasingly popular design where a company with abundant computing resources trains a deep neural network and offers query access for t…
PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators
Nils Lukas, Florian Kerschbaum
Deepfakes refer to content synthesized using deep generators, which, when misused, have the potential to erode trust in digital media. Synthesizing high-quality deepfakes requires…
Analyzing Leakage of Personally Identifiable Information in Language Models
Nils Lukas, Ahmed Salem, Robert Sim +3
Language Models (LMs) have been shown to leak information about training data through sentence-level membership inference and reconstruction attacks. Understanding the risk of LMs…