<p>The biometrics-enabled human authentication has emerged as one of the viable solutions to mitigate the challenges of traditional mechanisms that employ methods such as identity cards, PINs, passwords, tokens, secret codes, ATM cards, etc. Among all palmprint biometrics is one of the popular human authentication traits, which is specifically useful for visually impaired individuals. However, the biometric-based authentication system is vulnerable to a variety of spoofing assaulters. In this case, the attacker creates an artifact of the legitimate user by replicating the original replicas of their palmprint. This study seeks to investigate various palmprint spoof attacks and interpret their countermeasures ranging from traditional hardware-based approaches to contemporary Artificial Intelligence (AI)-inspired vision transformers (ViTs)-based techniques. Besides, we propose a novel taxonomy for the classification of anti-spoofing mechanisms, and the entire study is organized accordingly. Moreover, the underlying conceptions of anti-spoofing methods, performance analysis, and evaluation protocols are thoroughly illustrated. A comparative analysis of various benchmark datasets along with the performance metrics, as well as overall analysis, is also discussed. The study identified various open research issues along with future perspectives that can be explored by the investigators in this active field of research.</p>