9 research outputs found
Set-Membership Inference Attacks using Data Watermarking
In this work, we propose a set-membership inference attack for generative
models using deep image watermarking techniques. In particular, we demonstrate
how conditional sampling from a generative model can reveal the watermark that
was injected into parts of the training data. Our empirical results demonstrate
that the proposed watermarking technique is a principled approach for detecting
the non-consensual use of image data in training generative models.Comment: Preliminary wor
User interest prediction for tweets using semantic enrichment with DBpedia
online publicationstatus: publishe
Question Answering on Linked Data: Challenges and Future Directions
Question Answering (QA) systems are becoming the inspiring model for the future of search engines. While, recently, datasets underlying QA systems have been promoted from unstructured datasets to structured datasets with semantically highly enriched metadata, question answering systems are still facing serious challenges and are therefore not meeting users' expectations. This paper provides an exhaustive insight of challenges known so far for building QA systems, with a special focus on employing structured data (i.e. knowledge graphs).It thus helps researchers to easily spot gaps to fill with their future research agendas