The proliferation of generative models, such as Generative Adversarial Networks (GANs), Diffusion Models, and Variational Autoencoders (VAEs), has enabled the synthesis of high-quality multimedia data. However, these advancements have also raised significant concerns regarding adversarial attacks, unethical usage, and societal harm. Recognizing these challenges, researchers have increasingly focused on developing methodologies to detect synthesized data effectively, aiming to mitigate potential risks. Prior reviews have primarily focused on deepfake detection and often lack coverage of recent advancements in synthetic image detection, particularly methods leveraging multimodal frameworks for improved forensic analysis. To address this gap, the present survey provides a comprehensive review of state-of-the-art methods for detecting and classifying synthetic images generated by advanced generative AI models. This review systematically examines core detection methodologies, identifies commonalities among approaches, and categorizes them into meaningful taxonomies. Furthermore, given the crucial role of large-scale datasets in this field, we present an overview of publicly available datasets that facilitate further research and benchmarking in synthetic data detection.

翻译：生成模型（如生成对抗网络、扩散模型和变分自编码器）的广泛普及使得高质量多媒体数据的合成成为可能。然而，这些进展也引发了关于对抗攻击、不道德使用和社会危害的重大关切。认识到这些挑战，研究人员日益关注开发有效检测合成数据的方法，旨在减轻潜在风险。以往的综述主要集中于深度伪造检测，且往往缺乏对合成图像检测最新进展的覆盖，特别是利用多模态框架以改进取证分析的方法。为填补这一空白，本综述对检测和分类由先进生成式AI模型生成的合成图像的最新方法进行了全面回顾。本文系统性地审视了核心检测方法，识别了不同方法之间的共性，并将其归类为有意义的分类体系。此外，鉴于大规模数据集在该领域的关键作用，我们概述了公开可用的数据集，这些数据集有助于合成数据检测的进一步研究和基准测试。