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

TL;DR

This comprehensive review surveys recent methods for detecting AI-generated images, categorizing techniques, analyzing their effectiveness, and discussing future research directions to improve robustness and interpretability.

Contribution

It provides an up-to-date, systematic overview of detection paradigms, including multimodal and training-free approaches, filling gaps left by prior reviews.

Findings

Frequency-domain methods show robustness against adversarial attacks.

Multimodal reasoning-based frameworks enhance detection accuracy.

Hybrid approaches offer promising directions for future research.

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.