The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life. Although beneficial in numerous fields, this content presents significant risks, including potential misuse, societal disruptions, and ethical concerns. Consequently, detecting multimedia generated by LAIMs has become crucial, with a marked rise in related research. Despite this, there remains a notable gap in systematic surveys that focus specifically on detecting LAIM-generated multimedia. Addressing this, we provide the first survey to comprehensively cover existing research on detecting multimedia (such as text, images, videos, audio, and multimodal content) created by LAIMs. Specifically, we introduce a novel taxonomy for detection methods, categorized by media modality, and aligned with two perspectives: pure detection (aiming to enhance detection performance) and beyond detection (adding attributes like generalizability, robustness, and interpretability to detectors). Additionally, we have presented a brief overview of generation mechanisms, public datasets, and online detection tools to provide a valuable resource for researchers and practitioners in this field. Furthermore, we identify current challenges in detection and propose directions for future research that address unexplored, ongoing, and emerging issues in detecting multimedia generated by LAIMs. Our aim for this survey is to fill an academic gap and contribute to global AI security efforts, helping to ensure the integrity of information in the digital realm. The project link is https://github.com/Purdue-M2/Detect-LAIM-generated-Multimedia-Survey.
Expert Commentary: The Rise of AI-Generated Multimedia and the Need for Detection
The rapid advancement of Large AI Models (LAIMs) has ushered in a new era where AI-generated multimedia is becoming increasingly integrated into our daily lives. From text and images to videos and audio, these AI models have the ability to create highly realistic and convincing content. While this has numerous benefits in various fields, it also presents significant risks.
One of the key concerns surrounding AI-generated multimedia is the potential for misuse. In a world where anyone can create highly realistic fake videos, images, or text, the implications for misinformation and propaganda are immense. Detecting multimedia generated by LAIMs has therefore become crucial in ensuring the integrity of information in the digital realm.
In response to this need, researchers have been actively working on developing detection methods for LAIM-generated multimedia. However, despite the growing interest in this area, there has been a lack of systematic surveys that comprehensively cover the existing research. Addressing this gap, the authors of this article have provided the first survey that focuses specifically on detecting multimedia created by LAIMs.
The survey introduces a novel taxonomy for detection methods, categorized by media modality, such as text, images, videos, audio, and multimodal content. This taxonomy helps researchers and practitioners better understand the different approaches to detecting LAIM-generated multimedia. Additionally, the authors also highlight two perspectives: pure detection and beyond detection. Pure detection aims to enhance detection performance, while beyond detection adds attributes like generalizability, robustness, and interpretability to detectors.
Furthermore, the authors provide an overview of generation mechanisms, public datasets, and online detection tools, making this survey a valuable resource for those working in this field. By identifying current challenges in detection and proposing directions for future research, this survey aims to contribute not only to academic knowledge but also to global AI security efforts.
From a multidisciplinary perspective, this content touches upon various disciplines within the field of multimedia information systems. The integration of AI-generated multimedia into daily life requires a deep understanding of how different media modalities can be effectively detected. This involves knowledge from computer vision, natural language processing, signal processing, and human-computer interaction.
Moreover, the concepts presented in this survey are closely related to the wider fields of animations, artificial reality, augmented reality, and virtual reality. The ability to detect LAIM-generated multimedia becomes crucial in maintaining the trust and user experience in these immersive environments. Without proper detection mechanisms, these technologies run the risk of being misused and causing societal disruptions.
In conclusion, this comprehensive survey fills an academic gap and provides insights into detecting multimedia generated by LAIMs. With the rise of AI-generated content, it is essential to develop robust detection methods to ensure the reliability and integrity of information. By highlighting current research, challenges, and future directions, this survey contributes to the broader field of multimedia information systems and the development of secure AI technologies.
Reference:
Detect-LAIM-generated-Multimedia-Survey. Retrieved from https://github.com/Purdue-M2/Detect-LAIM-generated-Multimedia-Survey