Anti Spoofing

Anti-spoofing research aims to develop robust systems that can reliably distinguish genuine biometric data (e.g., faces, voices, fingerprints) from sophisticated spoof attempts. Current efforts concentrate on improving the generalization capabilities of anti-spoofing models across diverse datasets and unseen attack types, employing techniques like diffusion models, ensemble methods, and transfer learning with architectures such as ResNets, Vision Transformers, and Conformers. This field is crucial for securing biometric authentication systems in various applications, ranging from access control and financial transactions to identity verification, by mitigating the risks associated with presentation attacks.