  The tremendous recent advances in generative artificial intelligence
techniques have led to significant successes and promise in a wide range of
different applications ranging from conversational agents and textual content
generation to voice and visual synthesis. Amid the rise in generative AI and
its increasing widespread adoption, there has been significant growing concern
over the use of generative AI for malicious purposes. In the realm of visual
content synthesis using generative AI, key areas of significant concern has
been image forgery (e.g., generation of images containing or derived from
copyright content), and data poisoning (i.e., generation of adversarially
contaminated images). Motivated to address these key concerns to encourage
responsible generative AI, we introduce the DeepfakeArt Challenge, a
large-scale challenge benchmark dataset designed specifically to aid in the
building of machine learning algorithms for generative AI art forgery and data
poisoning detection. Comprising of over 32,000 records across a variety of
generative forgery and data poisoning techniques, each entry consists of a pair
of images that are either forgeries / adversarially contaminated or not. Each
of the generated images in the DeepfakeArt Challenge benchmark dataset
\footnote{The link to the dataset: http://anon\_for\_review.com} has been
quality checked in a comprehensive manner.
