Unmasking AI-created visual content: a review of generated images and deepfake detection technologies - Yupeng Zhang; Zongwei Pang; Shanyuan Huang; Chengyou Wang; Xiao Zhou (Journal of King Saud University: Computer and Information Sciences)
Abstract In this era, digital images and videos are ubiquitous in people’s lives, and generative models can easily produce high-quality images and videos. These images and videos enrich people’s lives and play important roles in various fields. However, maliciously generated images and videos can mislead the public, manipulate public opinion, invade privacy, and even lead to illegal activities. Therefore, detecting AI-created visual content has become a significant research topic in the field of multimedia information security. In recent years, the rapid development of deep learning technology has greatly accelerated the progress of AI-created visual content detection. This survey introduces the detection technologies for AI-created visual content that have developed in recent years, divided into two parts: AI-generated image detection and deepfake detection. In the AI-generated image detection section, we introduce current generative models and basic detection frameworks, and overview existing detection methods from the perspectives of unimodal and multimodal. In the deepfake detection section, we provide an overview of existing deepfake generation technique classifications, commonly used datasets, followed by some common evaluation metrics within the field. We also analyze the technical characteristics of existing methods based on the different feature information they utilize, summarizing and categorizing them. Finally, we propose future research directions and conclusions, offering suggestions for the development of AI-created visual content detection technologies.