Update references.bib

references.bib

@@ -281,7 +281,7 @@ @inproceedings{brack2023distilling
       Anote = {./images/brack2023distilling.png},
       title={Distilling Adversarial Prompts from Safety Benchmarks: Report for the Adversarial Nibbler Challenge},
       author={Manuel Brack and Patrick Schramowski and Kristian Kersting},
-      booktitle = {Working Notes of the AACL Workshop on the ART of Safety: Workshop on Adversarial testing and Red-Teaming for generative AI)},
+      booktitle = {Working Notes of the AACL Workshop on the ART of Safety(ARTS): Workshop on Adversarial testing and Red-Teaming for generative AI),
       OPTbooktitle = {Proceedings of the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 13th International Joint Conference on Natural Language Processing (IJCNLP-AACL)},
       year = {2023},
       Note = {Text-conditioned image generation models have recently achieved astonishing image quality and alignment results. Consequently, they are employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the web, they also produce unsafe content. As a contribution to the Adversarial Nibbler challenge, we distill a large set of over 1,000 potential adversarial inputs from existing safety benchmarks. Our analysis of the gathered prompts and corresponding images demonstrates the fragility of input filters and provides further insights into systematic safety issues in current generative image models.},