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

Arpan Mahara, Naphtali Rishe

TL;DR

This survey comprehensively catalogs and analyzes techniques for detecting AI-generated images across seven methodological categories, spanning spatial-domain, frequency-domain, fingerprint, patch-based, training-free, multimodal reasoning-based models, and commercial solutions. It highlights a clear progression from traditional pixel- and spectrum-based cues toward robust, cross-domain and cross-generator generalization, aided by multimodal and reasoning-driven architectures. Key contributions include a structured taxonomy, comparative analyses on public datasets, and discussions of open challenges such as generalizability, interpretability, and the need for unified benchmarking. The authors advocate hybrid approaches that combine the efficiency of training-free methods with the semantic and explanatory power of multimodal models to realize trustworthy and scalable synthetic-image forensics in real-world settings.

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.

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

TL;DR

This survey comprehensively catalogs and analyzes techniques for detecting AI-generated images across seven methodological categories, spanning spatial-domain, frequency-domain, fingerprint, patch-based, training-free, multimodal reasoning-based models, and commercial solutions. It highlights a clear progression from traditional pixel- and spectrum-based cues toward robust, cross-domain and cross-generator generalization, aided by multimodal and reasoning-driven architectures. Key contributions include a structured taxonomy, comparative analyses on public datasets, and discussions of open challenges such as generalizability, interpretability, and the need for unified benchmarking. The authors advocate hybrid approaches that combine the efficiency of training-free methods with the semantic and explanatory power of multimodal models to realize trustworthy and scalable synthetic-image forensics in real-world settings.

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.

Paper Structure

This paper contains 76 sections, 29 equations, 4 figures, 12 tables.

Table of Contents

  1. Introduction
  2. Reviews
  3. Spatial Domain Analysis / Spatial Feature Analysis Methods
  4. CNN-Generated Images Are Surprisingly Easy to Spot: Wang et al. (2020)
  5. Detection and Attribution of GAN Images: Goebel et al. (2020)
  6. Estimating Artifact Similarity with Representation Learning: Li et al. (2022), GASE-Net
  7. Local Intrinsic Dimensionality Analysis: Lorenz et al. (2023), AdaptedMultiLID
  8. AI-Generated Image Detection: Baraheem et al. (2023)
  9. Diffusion Reconstruction Error (DIRE): Wang et al. (2023)
  10. Classification and Explainable Identification: Bird and Lotfi (2024)
  11. Self-Contrastive Learning on ImagiNet: Boychev et al. (2024)
  12. Comparative Analysis of Spatial Domain Analysis Methods
  13. Multi-modal Vision-Language Methods (and/or Multi-modal Large Language Models)
  14. Universal Fake Image Detector by Ojha (2023)
  15. Language-Guided Synthetic Image Detection by Wu (2023)
  16. ...and 61 more sections

Figures (4)

  • Figure 1: Illustration of Simplified Architecture of GAN, Diffusion, VAE and Autoregressive Generative Models.
  • Figure 2: Categorization of Detection Methods Based on Core Architecture and Methodological Proposals. The figure illustrates the division of detection methods into categories. Each category is highlighted using blue-colored rectangles. Some methods are connected to multiple categories, shown using light-red highlights and arrows pointing to the respective sub-categories.
  • Figure 3: Taxonomy of Vision-Language Models (VLMs) categorized by learning paradigms.
  • Figure 4: Comparison of frequency domain transformations for real and generated images. The first column presents the input images, followed by their respective DFT, DWT, and DCT representations. The images were obtained from ForenSynth wang2020cnn, and the generated image was produced using CycleGAN zhu2017unpaired. These images are reproduced under the Attribution-NonCommercial-ShareAlike 4.0 International license.