Convolutional Neural Network for Detecting Deepfake Palmprint Images

With the rapid advancement of computer vision technology, various deepfake tools for generating deceptive images have emerged. Generative Adversarial Networks (GANs) can create various deceptive media streams, including images, audio, and video, leading to numerous societal challenges. Palmprint recognition technology has recently been applied in financial identity verification, particularly in confirming transactions across various banking platforms. Manipulating critical financial transactions or generating malicious images to deceive authentication processes can result in significant disruptions. Convolutional Neural Networks (CNNs) are considered practical tools. We propose the implementation of a Dual Cascade Convolutional Neural Network (DC-CNN) algorithm that utilizes a dual-channel technique. This approach involves two networks that train one subnetwork and then apply the same configuration to the other. The feature vectors are combined, the fake inputs can be identified. This dual-channel technique is particularly effective for detecting forged images. Our approach involves comparing various CNN architectures, such as MesoNet, MesoInceptionNet, and Dense CNN (D-CNN), within the framework of GAN methods, such as Wasserstein GAN (WGAN) and Cycle GAN. In our experiments, DC-CNN demonstrates favorable results in detecting fake palmprints based on WGAN and cycle GAN. Specifically, for WGAN-based fake palmprints, the model achieved the weighted precision of 90.83%, weighted recall of 90.20%, weighted F1 scores of 89.92% and accuracy of 90.20%. In the case of Cycle GAN-based fake palmprints, the model exhibited the weighted precision of 87.86%, weighted recall of 87.91%, weighted F1 scores of 87.85% and accuracy of 87.91%. Therefore, DC-CNN emerges as a promising approach in the fields of deepfake palmprint detection and identity verification.