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.

翻译：生成式人工智能技术近年来的巨大进展，已在对话代理、文本内容生成、语音与视觉合成等广泛领域取得了显著成功并展现出巨大潜力。随着生成式AI的兴起及其日益广泛的应用，人们对其可能被用于恶意目的的担忧也显著增长。在利用生成式AI进行视觉内容合成的领域中，图像伪造（例如生成包含或衍生自受版权保护内容的图像）与数据投毒（即生成对抗性污染的图像）已成为备受关注的关键问题。为应对这些关键问题以促进负责任的生成式AI发展，我们推出了DeepfakeArt挑战赛——一个专门设计的大规模挑战基准数据集，旨在助力构建用于检测生成式AI艺术伪造与数据投毒的机器学习算法。该数据集涵盖多种生成式伪造与数据投毒技术，包含超过32,000条记录，每条记录由一对图像组成，分别标记为伪造/对抗污染或正常图像。DeepfakeArt挑战赛基准数据集中的每张生成图像均已通过全面质量检查\footnote{数据集链接：http://anon\_for\_review.com}。