Methods and Trends in Detecting AI-Generated Images: A Comprehensive Review

Authors: Arpan Mahara, Naphtali Rishe

Published: 2025-02-21 03:16:18+00:00

Comment: 34 pages, 4 Figures, 10 Tables

AI Summary

This comprehensive survey reviews state-of-the-art techniques for detecting and classifying AI-generated synthetic images, addressing limitations of prior reviews by incorporating recent advancements like multimodal frameworks and training-free methodologies. It systematically categorizes detection paradigms (spatial-domain, frequency-domain, fingerprint-based, patch-based, training-free, and multimodal reasoning) and provides comparative analyses on publicly available datasets to assess their generalizability, robustness, and interpretability. The paper also highlights open challenges and future directions, advocating for hybrid frameworks that combine efficiency with semantic reasoning for trustworthy and explainable synthetic image forensics.

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 predominantly focused on deepfake detection and often overlook recent advancements in synthetic image forensics, particularly approaches that incorporate multimodal frameworks, reasoning-based detection, and training-free methodologies. To bridge this gap, this survey provides a comprehensive and up-to-date review of state-of-the-art techniques for detecting and classifying synthetic images generated by advanced generative AI models. The review systematically examines core detection paradigms, categorizes them into spatial-domain, frequency-domain, fingerprint-based, patch-based, training-free, and multimodal reasoning-based frameworks, and offers concise descriptions of their underlying principles. We further provide detailed comparative analyses of these methods on publicly available datasets to assess their generalizability, robustness, and interpretability. Finally, the survey highlights open challenges and future directions, emphasizing the potential of hybrid frameworks that combine the efficiency of training-free approaches with the semantic reasoning of multimodal models to advance trustworthy and explainable synthetic image forensics.

Key findings

Multimodal frameworks, especially those integrating vision-language and large language models, exhibit greater robustness, adaptability, and explainability in detecting AI-generated images compared to traditional methods. There is a clear methodological evolution from low-level artifact detection to semantically informed and reasoning-based systems. Hybrid frameworks combining the efficiency of training-free methods with the reasoning capabilities of multimodal models are a promising future direction for robust, interpretable, and real-time detection.

Approach

This paper is a comprehensive review (survey) that systematically examines and categorizes state-of-the-art techniques for detecting and classifying synthetic images generated by advanced AI models. It provides detailed descriptions of underlying principles, comparative analyses on public datasets, and discusses open challenges and future research directions.

Datasets

ForenSynths, Artifact Dataset, SynthBuster, DiffusionForensics Dataset, UnivFD Dataset, Community Forensics, GenImage, CIFAKE Dataset, AIGCDetection Benchmark Dataset, ImagiNet Dataset, Chameleon Dataset, RAISE-1k

Model(s)

UNKNOWN

Author countries

USA

← Previous