Mastering Deepfake Detection: A Cutting-Edge Approach to Distinguish GAN and Diffusion-Model Images
Open accessAbstract
Detecting and recognizing deepfakes is a pressing issue in the digital age. In this study, we first collected a dataset of pristine images and fake ones properly generated by nine different Generative Adversarial Network (GAN) architectures and four Diffusion Models (DM). The dataset contained a total of 83,000 images, with equal distribution between the real and deepfake data. Then, to address different deepfake detection and recognition tasks, we proposed a hierarchical multi-level approach. At the first level, we classified real images from AI-generated ones. At the second level, we distinguished between images generated by GANs and DMs. At the third level (composed of two additional sub-levels), we recognized the specific GAN and DM architectures used to generate the synthetic data. Experimental results demonstrated that our approach achieved more than 97% classification accuracy, outperforming existing state-of-the-art methods. The models obtained in the different levels turn out to be robust to various attacks such as JPEG compression (with different quality factor values) and resize (and others), demonstrating that the framework can be used and applied in real-world contexts (such as the analysis of multimedia data shared in the various social platforms) for support even in forensic investigations to counter the illicit use of these powerful and modern generative models. We are able to identify the specific GAN and DM architecture used to generate the image, which is critical in tracking down the source of the deepfake. Our hierarchical multi-level approach to deepfake detection and recognition shows promising results in identifying deepfakes allowing focus on underlying task by improving (about 2% on the average) standard multiclass flat detection systems. The proposed method has the potential to enhance the performance of deepfake detection systems, aid in the fight against the spread of fake images, and safeguard the authenticity of digital media.
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Traditional detection methods rely on classical machine learning models and often struggle with generalization and scalability when confronted with the latest GANs and advanced deep learning architectures.
“Traditional detection methods [3], [4] rely on classical machine learning models; however, these approaches often struggle with generalization and scalability when confronted with the latest generative adversarial networks (GANs) and advanced deep learning architectures that are used to create deepfakes [5].”
From Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection (2025) · cites this paper as [5]
The Discussion and Future Works section says state-of-the-art methods can fail to generalize and that classification performance degrades as more generative architectures are included, mainly because of diffusion-model images. It does not establish a scalability problem, and the Comparison with State of the Art Methods section says the tested methods generalized well when distinguishing among GAN architectures. The statement about traditional methods relying on classical machine learning is attached to citations [3] and [4], not this source [5]. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCurrent state-of-the-art methods have proven to be very effective in detecting deepfake content generated by GAN architectures and Diffusion Models (DMs), demonstrating the ability to specialize not only in recognizing the architecture but also in identifying the specific pattern within the creation process.
“Current state-of-the-art (SOTA) methods have proven to be very effective in detecting deepfake content generated by GAN architectures [93,94,95] and Diffusion Models (DMs) [96,97], demonstrating the ability to specialize not only in recognizing the architecture but also in identifying the specific pattern within the creation process.”
From Deepfake Media Forensics: Status and Future Challenges (2025) · cites this paper as [96]
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- Authors
- Guarnera, L., Giudice, O., & Battiato, S.
- Affiliations
- University of Catania (IT)
- Bank of Italy (IT)
- Venue
- ACM Transactions on Multimedia Computing, Communications, and Applications
- Published
- 2024-09-12
- Volume
- 20
- Issue
- 11
- Pages
- 1-24
- ISSN
- 1551-6857 (ISSN-L)
- 1551-6865
- OpenAlex
- W4392623241
- Cited by
- 74
- References
- 36
- Citations by year
- 2026: 24 · 2025: 36 · 2024: 14
- FWCI
- 10.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Transactions on Multimedia Computing Communications and Applications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, generative adversarial networks, diffusion models, JPEG compression robustness, image forensics
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