Using Local Phase Quantization to Identify Fake Faces in Online Social Networks

The rapid advancement of Generative AI, especially Generative Adversarial Networks (GANs), has increased the issue of fake news on Online Social Networks (OSNs) by generating deceptive face images for social media profiles. Although existing detection methods are accurate, their effectiveness decreases when images are post-processed, which is common on OSNs. In this paper, we present LPQ-Net, a model combining Local Phase Quantization (LPQ) for feature extraction with a CNN-based classifier. We explore two variants: one sets a new benchmark in detecting StyleGAN2-generated images, and the other excels in identifying images shared on Facebook, WhatsApp, and Instagram. LPQ-Net also operates with minimal parameters, outperforming state-of-the-art methods and making it ideal for resource-constraint applications. Furthermore, our solution demonstrates its effectiveness by performing exceptionally well in detecting images generated by various Diffusion models. We further show that incorporating LPQ features into fine-tuned classifiers like ResNet50, ResNet101, InceptionV3, and DenseNet121 significantly improves performance.