55 citations · 220 across the 35 of their papers we have counts for
36 papers
Backdooring Textual Inversion for Concept Censorship
Yutong Wu, Jie Zhang, Florian Kerschbaum +1
Recent years have witnessed success in AIGC (AI Generated Content). People can make use of a pre-trained diffusion model to generate images of high quality or freely modify existin…
Identifying and Mitigating the Security Risks of Generative AI
Clark Barrett, Brad Boyd, Elie Burzstein +20
Every major technical invention resurfaces the dual-use dilemma -- the new technology has the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, s…
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…
Towards Robust Dataset Learning
Yihan Wu, Xinda Li, Florian Kerschbaum +2
Adversarial training has been actively studied in recent computer vision research to improve the robustness of models. However, due to the huge computational cost of generating adv…