Generated Content
Generated content (AIGC), encompassing AI-produced text, images, and video, is a rapidly evolving field focused on developing and evaluating methods for creating and identifying such content. Current research emphasizes improving the quality and realism of AIGC, developing robust detection methods to mitigate misuse (often employing techniques like CLIP and diffusion models), and exploring ethical considerations such as copyright and bias. This area is significant due to its potential to revolutionize creative industries and its implications for misinformation, intellectual property, and the broader societal impact of AI.
Papers
Watermark-based Detection and Attribution of AI-Generated Content
Zhengyuan Jiang, Moyang Guo, Yuepeng Hu, Neil Zhenqiang Gong
AI Royalties -- an IP Framework to Compensate Artists & IP Holders for AI-Generated Content
Pablo Ducru, Jonathan Raiman, Ronaldo Lemos, Clay Garner, George He, Hanna Balcha, Gabriel Souto, Sergio Branco, Celina Bottino
The Future of Combating Rumors? Retrieval, Discrimination, and Generation
Junhao Xu, Longdi Xian, Zening Liu, Mingliang Chen, Qiuyang Yin, Fenghua Song
A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles
Jiani Fan, Minrui Xu, Ziyao Liu, Huanyi Ye, Chaojie Gu, Dusit Niyato, Kwok-Yan Lam
Dynamic Explanation Emphasis in Human-XAI Interaction with Communication Robot
Yosuke Fukuchi, Seiji Yamada
From Perils to Possibilities: Understanding how Human (and AI) Biases affect Online Fora
Virginia Morini, Valentina Pansanella, Katherine Abramski, Erica Cau, Andrea Failla, Salvatore Citraro, Giulio Rossetti