[论文解读] Detecting Multimedia Generated by Large AI Models: A Survey

Lin Li, Neeraj Gupta+8 more|arXiv (Cornell University)|Jan 22, 2024

Digital Media Forensic Detection被引用 5

一句话总结

本论文首次对跨文本、图像、视频、音频与多模态内容的大型AI模型生成多媒体（LAIMs）检测进行全面综述，提出新的双向分类法与资源概览。

ABSTRACT

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, online detection tools, and evaluation metrics to provide a valuable resource for researchers and practitioners in this field. Most importantly, we offer a focused analysis from a social media perspective to highlight their broader societal impact. 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.

研究动机与目标

• 系统性地概览 LAIM 生成的跨所有模态（文本、图像、视频、音频、多模态）的多媒体检测。
• 提出基于模态的检测方法新分类法及两种视角（纯检测与超越检测）。
• 总结生成机制、公开数据集以及与检测相关的在线工具。
• 识别当前挑战并提出未来研究方向，以提升检测鲁棒性与泛化性。

提出的方法

• 提出两种视角的分类法：纯检测（以检测为焦点）和超越检测（如泛化性、鲁棒性、可解释性等属性）。
• 按模态对检测器进行分类，并进一步按检测类别（文本、图像、视频、音频、多模态）以及按技术方法（例如水印、伪影、风格计量、困惑度、归因、定位）进行分类。
• 将文本检测的方法区分为易解释与难解释，并将其他模态的方法分为正式类别（例如基于频率、基于时空、基于扩散的指纹）。
• 调查跨模态的 LAIM 生成媒体的公开数据集与在线工具，详细说明来源、内容类型和生成方法。

实验结果

研究问题

• RQ1 在文本、图像、视频、音频和多模态内容方面，存在哪些 LAIM 生成媒体的检测技术？
• RQ2 如何将检测器组织起来，同时体现检测性能与其他理想属性（泛化性、鲁棒性、可解释性）？
• RQ3 用于评估和应用 LAIM 生成的多媒体检测器的关键数据集和在线工具有哪些？

主要发现

• 一种新颖的分类法将跨所有模态的检测方法分为纯检测和超越检测。
• 文本检测分为易解释和难解释的方法，包括水印、伪影、风格计量、困惑度和归因。
• 存在大量用于图像和文本生成检测的公开数据集，包括 GenImage、DiffusionDB、HC3 等；也存在全面的多模态数据集。
• 该综述汇总了在线检测工具，并强调提升检测器的鲁棒性、泛化性和可解释性的挑战与未来方向。