2 citations · 3 across the 5 of their papers we have counts for
6 papers
TwinBreak: Jailbreaking LLM Security Alignments based on Twin Prompts
Torsten Krauß, Hamid Dashtbani, Alexandra Dmitrienko
Machine learning is advancing rapidly, with applications bringing notable benefits, such as improvements in translation and code generation. Models like ChatGPT, powered by Large L…
Memory Backdoor Attacks on Neural Networks
Eden Luzon, Guy Amit, Roy Weiss +3
Neural networks are often trained on proprietary datasets, making them attractive attack targets. We present a novel dataset extraction method leveraging an innovative training tim…
DNNShield: Embedding Identifiers for Deep Neural Network Ownership Verification
Jasper Stang, Torsten Krauß, Alexandra Dmitrienko
The surge in popularity of machine learning (ML) has driven significant investments in training Deep Neural Networks (DNNs). However, these models that require resource-intensive t…
ClearMark: Intuitive and Robust Model Watermarking via Transposed Model Training
Torsten Krauß, Jasper Stang, Alexandra Dmitrienko
Due to costly efforts during data acquisition and model training, Deep Neural Networks (DNNs) belong to the intellectual property of the model creator. Hence, unauthorized use, the…
Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations
Torsten Krauß, Alexandra Dmitrienko
Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through div…
CrowdGuard: Federated Backdoor Detection in Federated Learning
Phillip Rieger, Torsten Krauß, Markus Miettinen +2
Federated Learning (FL) is a promising approach enabling multiple clients to train Deep Neural Networks (DNNs) collaboratively without sharing their local training data. However, F…