Wavelet-based GAN Fingerprint Detection using ResNet50

• URL: http://arxiv.org/abs/2510.21822v1
• Date: Tue, 21 Oct 2025 22:40:16 GMT
• Title: Wavelet-based GAN Fingerprint Detection using ResNet50
• Authors: Sai Teja Erukude, Suhasnadh Reddy Veluru, Viswa Chaitanya Marella,
• Abstract summary: Generative Adversarial Networks (GANs) have become a significant challenge in digital image forensics.<n>This research presents a wavelet-based detection method that uses discrete wavelet transform preprocessing.<n>The method proposed illustrates the effectiveness of wavelet-domain analysis to detect GAN images and emphasizes the potential of further developing the capabilities of future deepfake detection systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying images generated by Generative Adversarial Networks (GANs) has become a significant challenge in digital image forensics. This research presents a wavelet-based detection method that uses discrete wavelet transform (DWT) preprocessing and a ResNet50 classification layer to differentiate the StyleGAN-generated images from real ones. Haar and Daubechies wavelet filters are applied to convert the input images into multi-resolution representations, which will then be fed to a ResNet50 network for classification, capitalizing on subtle artifacts left by the generative process. Moreover, the wavelet-based models are compared to an identical ResNet50 model trained on spatial data. The Haar and Daubechies preprocessed models achieved a greater accuracy of 93.8 percent and 95.1 percent, much higher than the model developed in the spatial domain (accuracy rate of 81.5 percent). The Daubechies-based model outperforms Haar, showing that adding layers of descriptive frequency patterns can lead to even greater distinguishing power. These results indicate that the GAN-generated images have unique wavelet-domain artifacts or "fingerprints." The method proposed illustrates the effectiveness of wavelet-domain analysis to detect GAN images and emphasizes the potential of further developing the capabilities of future deepfake detection systems.

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