Iris recognition systems are vulnerable to presentation assaults, such as textured contact lenses or printed images. This chapter summarizes significant advancements in spoof attack detection and iris identification techniques via a thorough examination of current research projects. The research shows a wide range of methods, including dense convolution neural networks, multispectral iris sensors, and software-based techniques. Detecting changing spoofing strategies, cross-database generalization, and the effect of eye wear on detection accuracy are among the noteworthy issues that the study covers. The field’s dynamic progress is shown by the use of deep learning models, the research of gaze-oriented approaches, and the integration of numerous biometric modalities. Even with advancements, there are still issues that need to be resolved, such as the need for explanation in complicated models and the necessity for adaptive systems to fight new threats. The most recent advancements in iris anti-spoofing detection algorithms, encompassing both manually designed methods and deep learning-based methods, are thoroughly examined in this chapter. We go over the difficulties associated with various strategies and emphasize the value of benchmark datasets in assessing the effectiveness of iris anti-spoofing detection techniques. We also present a summary of the most recent evaluation measures and benchmark datasets utilized in the field. Lastly, this chapter outlines possible future research directions and uses for iris anti-spoofing detection, including multi-model techniques and adversarial training.