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 has been quality checked in a comprehensive manner. The DeepfakeArt Challenge is a core part of GenAI4Good, a global open source initiative for accelerating machine learning for promoting responsible creation and deployment of generative AI for good.

翻译：生成式人工智能技术的近期重大进展已在从对话系统、文本内容生成到语音与视觉合成等广泛领域取得显著成功与巨大潜力。随着生成式AI的兴起及日益广泛的应用，人们对其被滥用于恶意目的之担忧显著加剧。在利用生成式AI进行视觉内容合成方面，两大核心关切领域为图像伪造（例如生成包含或衍生自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）。为应对这些关键挑战以推动负责任的生成式AI发展，我们提出DeepfakeArt挑战——一个专门设计用于辅助构建生成式AI艺术伪造与数据投毒检测机器学习算法的大规模基准数据集。该数据集涵盖32,000余条记录，涉及多种生成式伪造与数据投毒技术，每条条目包含成对图像，并标注是否属于伪造/对抗性污染图像。DeepfakeArt挑战基准数据集中的所有生成图像均经过全面质量审查。该挑战是GenAI4Good全球开源计划的核心组成部分，旨在加速机器学习以促进生成式AI负责任地创建与部署。