Methods and Trends in Detecting Generated Images: A Comprehensive Review

2025-02-21

AI Summary

The rapid advancement of generative AI—particularly GANs, diffusion models, and VAEs—has significantly intensified the risks and societal harms associated with synthetic imagery. While existing surveys predominantly focus on deepfake detection, they lack systematic coverage of multimodal digital forensics and emerging synthetic image identification techniques. To address this gap, we propose the first taxonomy of synthetic image detection methods explicitly designed for multimodal frameworks. Our survey comprehensively analyzes over 100 representative works published between 2019 and 2024, spanning key paradigms including frequency-domain analysis, texture anomaly modeling, neural artifact identification, cross-modal alignment, self-supervised pretraining, and large-model zero-shot discrimination. We consolidate more than ten mainstream public benchmarks into a structured knowledge graph, enabling rigorous algorithmic development, standardized benchmarking, and robustness evaluation. This work provides both theoretical foundations and practical guidance for advancing trustworthy multimodal forensic research.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

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.

Problem

• Detecting synthetic images from advanced AI models
• Addressing adversarial attacks and unethical usage
• Reviewing multimodal frameworks for forensic analysis

Innovation

• GANs for image synthesis
• Multimodal forensic analysis
• Synthetic image detection methodologies