# A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions
## 1 Introduction  
AI-synthesized tools have recently been developed with the ability to generate convincing voices [1]. However, while these tools were introduced to help people, they have also been used to spread disinformation around the world using audio [2], and their malicious use has led to fear of the \"Audio Deepfake.\" Audio Deepfakes, recently called audio manipulations, are becoming widely accessible using simple mobile devices or personal PCs [3]. This has led to worldwide public cybersecurity concerns regarding the side effects of using AD. Regardless of the benefit of this technology, ADs go beyond a simple text message or an email link. People can use it as a logical-access voice spoofing technique [4], where it can be used to manipulate public opinion for propaganda, defamation, or even terrorism. Massive amounts of voice recordings are broadcast daily over the Internet, and detecting fakeness from them is a challenging task [5]. However, AD attackers have targeted not only individuals and organizations but also politicians and governments [6]. In 2019, fraudsters used AI-based software to impersonate a CEO's voice and swindled more than USD 243,000 via a telephone call [7]. For this reason, we need to authenticate any distributed audio recordings to avoid spreading disinformation. This problem has thus been of significant interest to the research community in recent years. Three types of AD haveemerged, increasing the challenge in detection; they are synthetic-based, imitation-based, and replay-based, as will be explained in the following section. With regard to Deepfakes, many detection methods have been introduced to discern fake audio files from real speech. A number of ML and DL models have been developed that use different strategies to detect fake audio. The following strategies describe the AD detection process in general, as illustrated in Figure 1. First, each audio clip should be preprocessed and transformed into suitable audio features, such as Mel-spectrograms. These features are input into the detection model, which then performs the necessary operations, such as the training process. The output is fed into any fully connected layer with an activation function (for a nonlinearity task) to produce a prediction probability of class 0 as fake or class 1 as real. However, there is a trade-off between accuracy and computational complexity. Further work is therefore required to improve the performance of AD detection and overcome the gaps identified in the literature. AD detection has therefore become an active area of research with the development of advanced techniques and DL methods. However, with such advancements, current DL methods are struggling, and further investigation is necessary to understand what area of AD detection needs further development. Moreover, a comparative analysis of current methods is also important, and to the best of the authors' knowledge, a review of imitated and synthetically generated audio detection methods is missing from the literature. Thus, this article introduces the following significant contributions to the literature:  
* A review of state-of-the-art AD detection methods that target imitated and synthetically generated voices;
* provision of a brief description of current AD datasets;
* a comparative analysis of existing methods and datasets to highlight the strengths and weaknesses of each AD detection family;
* a quantitative comparison of recent state-of-the-art AD detection methods; and
* a discussion of the challenges and potential future research directions in this area. The rest of this article is organized as follows. An AD definition and its types are presented in Section 2. Section 3 discusses and summarizes the current methods developed for AD detection. Section 4 presents the generated audio dataset used for AD detection and highlights its characteristics. Section 5 presents a quantitative comparison of recent state-of-the-art AD detection methods.