Fourier-Based GAN Fingerprint Detection using ResNet50

The rapid rise of photorealistic images produced from Generative Adversarial Networks (GANs) poses a serious challenge for image forensics and industrial systems requiring reliable content authenticity. This paper uses frequency-domain analysis combined with deep learning to solve the problem of distinguishing StyleGAN-generated images from real ones. Specifically, a two-dimensional Discrete Fourier Transform (2D DFT) was applied to transform images into the Fourier domain, where subtle periodic artifacts become detectable. A ResNet50 neural network is trained on these transformed images to differentiate between real and synthetic ones. The experiments demonstrate that the frequency-domain model achieves a 92.8 percent and an AUC of 0.95, significantly outperforming the equivalent model trained on raw spatial-domain images. These results indicate that the GAN-generated images have unique frequency-domain signatures or "fingerprints". The method proposed highlights the industrial potential of combining signal processing techniques and deep learning to enhance digital forensics and strengthen the trustworthiness of industrial AI systems.

Key Contributions

• Applies 2D Discrete Fourier Transform to expose periodic GAN upsampling artifacts invisible in the spatial domain
• Demonstrates ResNet50 trained on frequency-domain representations achieves 92.8% accuracy and AUC 0.95 detecting StyleGAN images, significantly outperforming spatial-domain equivalents
• Empirically validates that GAN-generated images possess unique frequency-domain 'fingerprints' exploitable for reliable deepfake detection

🛡️ Threat Analysis

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