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The tremendous recent advances in generative artificial intelligencetechniques have led to significant successes and promise in a wide range ofdifferent applications ranging from conversational agents and textual contentgeneration to voice and visual synthesis. Amid the rise in generative AI andits increasing widespread adoption, there has been significant growing concernover the use of generative AI for malicious purposes. In the realm of visualcontent synthesis using generative AI, key areas of significant concern hasbeen image forgery (e.g., generation of images containing or derived fromcopyright content), and data poisoning (i.e., generation of adversariallycontaminated images). Motivated to address these key concerns to encourageresponsible generative AI, we introduce the DeepfakeArt Challenge, alarge-scale challenge benchmark dataset designed specifically to aid in thebuilding of machine learning algorithms for generative AI art forgery and datapoisoning detection. Comprising of over 32,000 records across a variety ofgenerative forgery and data poisoning techniques, each entry consists of a pairof images that are either forgeries / adversarially contaminated or not. Eachof the generated images in the DeepfakeArt Challenge benchmark dataset has beenquality checked in a comprehensive manner. The DeepfakeArt Challenge is a corepart of GenAI4Good, a global open source initiative for accelerating machinelearning for promoting responsible creation and deployment of generative AI forgood.

DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection