TOWARDS SECURE BIOMETRICS: DEEP FAKE PALMPRINT DETECTION WITH ADVANCED CNN-RESNET MODELS

The increasing sophistication of deepfake technology poses a substantial risk to biometric security, particularly in palmprint recognition systems. As palmprint authentication gains widespread adoption in financial transactions and identity verification, the potential for deepfake-based spoofing attacks becomes a major concern. Fraudsters can manipulate or synthetically generate palmprint images to bypass authentication systems, leading to financial fraud and compromised security. This research focuses on the development of an effective deepfake palmprint detection framework leveraging deep learning techniques. A combination of Convolutional Neural Networks (CNNs) and advanced architectures such as ResNet and custom-designed models is employed to extract distinguishing features and enhance classification accuracy. To improve model robustness, a unique dataset is created by generating deepfake palmprints using ridge extraction and blending techniques, followed by augmentation with transformations like contrast variation, random rotation, perspective distortion, and noise addition. The dataset is systematically split for training, validation, and testing to ensure comprehensive evaluation. Performance metrics including accuracy, precision, recall, and F1-score are used to assess model effectiveness, with a focus on achieving high detection rates. This research contributes to strengthening biometric security by providing a reliable solution to detect deepfake palmprints, particularly in high-risk applications such as banking and secure authentication systems.