  The availability of smart devices leads to an exponential increase in
multimedia content. However, the rapid advancements in deep learning have given
rise to sophisticated algorithms capable of manipulating or creating multimedia
fake content, known as Deepfake. Audio Deepfakes pose a significant threat by
producing highly realistic voices, thus facilitating the spread of
misinformation. To address this issue, numerous audio anti-spoofing detection
challenges have been organized to foster the development of anti-spoofing
countermeasures. This survey paper presents a comprehensive review of every
component within the detection pipeline, including algorithm architectures,
optimization techniques, application generalizability, evaluation metrics,
performance comparisons, available datasets, and open-source availability. For
each aspect, we conduct a systematic evaluation of the recent advancements,
along with discussions on existing challenges. Additionally, we also explore
emerging research topics on audio anti-spoofing, including partial spoofing
detection, cross-dataset evaluation, and adversarial attack defence, while
proposing some promising research directions for future work. This survey paper
not only identifies the current state-of-the-art to establish strong baselines
for future experiments but also guides future researchers on a clear path for
understanding and enhancing the audio anti-spoofing detection mechanisms.
