This paper provides a comprehensive survey of audio anti-spoofing detection, focusing on the development and evaluation of algorithms to combat Deepfake audio. The authors review various aspects of the detection pipeline, including algorithm architectures, optimization techniques, application generalizability, evaluation metrics, performance comparisons, available datasets, and open-source availability. They highlight the importance of both fully and partially spoofed audio detection, the effectiveness of optimization techniques such as data augmentation and loss function choices, and the integration of speaker verification with anti-spoofing models. The survey also explores emerging research topics like adversarial attack defense and cross-dataset evaluation, and provides open-source information for all reviewed models and datasets. The paper aims to establish strong baselines and guide future researchers in understanding and enhancing audio anti-spoofing detection mechanisms.This paper provides a comprehensive survey of audio anti-spoofing detection, focusing on the development and evaluation of algorithms to combat Deepfake audio. The authors review various aspects of the detection pipeline, including algorithm architectures, optimization techniques, application generalizability, evaluation metrics, performance comparisons, available datasets, and open-source availability. They highlight the importance of both fully and partially spoofed audio detection, the effectiveness of optimization techniques such as data augmentation and loss function choices, and the integration of speaker verification with anti-spoofing models. The survey also explores emerging research topics like adversarial attack defense and cross-dataset evaluation, and provides open-source information for all reviewed models and datasets. The paper aims to establish strong baselines and guide future researchers in understanding and enhancing audio anti-spoofing detection mechanisms.