| Title: | Detection of AI-generated synthetic images with a lightweight CNN |
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| Authors: | ID Lokner Lađević, Adrian (Author)
ID Kramberger, Tin (Author)
ID Kovačević, Renata (Author)
ID Vlahek, Dino (Author) |
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| Files: |  ai-05-00076-v2.pdf (36,84 MB)
MD5: AF62811927CC5E07C613F637C5D3B637
 https://www.mdpi.com/2673-2688/5/3/76
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | The rapid development of generative adversarial networks has significantly advanced the generation of synthetic images, presenting valuable opportunities and ethical dilemmas in their potential misuse across various industries. The necessity to distinguish real from AI-generated content is becoming increasingly critical to preserve the integrity of online data. While traditional methods for detecting fake images resulting from image tampering rely on hand-crafted features, the sophistication of manipulated images produced by generative adversarial networks requires more advanced detection approaches. The lightweight approach proposed here is based on convolutional neural networks that comprise only eight convolutional and two hidden layers that effectively differentiate AI-generated images from real ones. The proposed approach was assessed using two benchmark datasets and custom-generated data from Sentinel-2 imagery. It demonstrated superior performance compared to four state-of-the-art methods on the CIFAKE dataset, achieving the highest accuracy of 97.32%, on par with the highest-performing state-of-the-art method. Explainable AI is utilized to enhance our comprehension of the complex processes involved in synthetic image recognition. We have shown that, unlike authentic images, where activations often center around the main object, in synthetic images, activations cluster around the edges of objects, in the background, or in areas with complex textures. |
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| Keywords: | convolutional neural networks, generative adversarial networks, classification, synthetic images, explanable artificial intelligence |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 08.08.2024 |
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| Article acceptance date: | 30.08.2024 |
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| Publication date: | 03.09.2024 |
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| Publisher: | MDPI |
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| Year of publishing: | 2024 |
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| Number of pages: | str. 1575-1593 |
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| Numbering: | Vol. 5, issue 3 |
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| PID: | 20.500.12556/DKUM-91710  |
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| UDC: | 004.9 |
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| ISSN on article: | 2673-2688 |
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| COBISS.SI-ID: | 206834435 |
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| DOI: | 10.3390/ai5030076 |
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| Copyright: | © 2024 by the authors |
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| Publication date in DKUM: | 29.01.2025 |
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| Views: | 297 |
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| Downloads: | 16 |
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| Metadata: |    |
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| Categories: | Misc.
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