# A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions
## 2 Types of Audio Deepfake Attacks  
AD technology is a recent invention that allows users to create audio clips that sound like specific people saying things they did not say [2]. This technology was initially developed for a variety of applications intended to improve human life, such as audiobooks, where it could be used to imitate soothing voices [8]. As defined from the AD literature, there are three main types of audio fakeness: imitation-based, synthetic-based, and replay-based Deepfakes.  
Imitation-based Deepfakes are \"a way of transforming speech (secret audio) so that it sounds like another speech (target audio) with the primary purpose of protecting the privacy of the secret audio\" [3]. Voices can be imitated in different ways, for example, by using humans with similar voices who are able to imitate the original speaker. However, masking algorithms, such as Efficient Wavelet Mask (EWM), have been introduced to imitate audio and Deepfake speech. In particular, an original and target audio will be recorded with similar characteristics. Then, as illustrated in Figure 2, the signal of the original audio Figure 2a will be transformed to say the speech in the target audio in Figure 2b using an imitation generation method that will generate a new speech, shown in Figure 2c, which is the fake one. It is thus difficult for humans to discern between the fake and real audio generated by this method [3].  
Synthetic-based or Text-To-Speech (TTS) aims to transform text into acceptable and natural speech in real time [9] and consists of three modules: a text analysis model, an acoustic model, and a vocoder. To generate synthetic Deepfake audio, two crucial steps should be followed. First, clean and structured raw audio should be collected, with a transcript text of the audio speech. Second, the TTS model must be trained using the collected data to build a synthetic audio generation model. Tactoran 2, Deep Voice 3, and FastSpeech 2 are well-known model generation techniques and are able to produce the highest level of natural-sounding audio [10; 11]. Tactoran 2 creates Mel-spectrograms with a modified WaveNet vocoder [12]. Deep Voice 3 is a neural text-to-speech model that uses a position-augmented attention mechanism for an attention-based decoder [13]. FastSpeech 2 produces high-quality results with the fastest training time [11]. In the synthetic technique, the transcript text with the voice of the target speaker will be fed into the generation model. The text analysis module then processes the incoming text and converts it into linguistic characteristics. Then, the acoustic module extracts the parameters of the target speaker from the dataset depending on the linguistic features generated from the text analysis module. Last, the vocoder will learn to create speech waveforms based on the acoustic  
Figure 2: Imitation-based Deepfake.  
feature parameters, and the final audio file will be generated, which includes the synthetic fake audio in a waveform format. Figure 3 illustrates the process of synthetic-based voice generation.  
Replay-based Deepfakes are a type of malicious work that aims to replay a recording of the target speaker's voice [14]. There are two types: far-field detection and cut-and-paste detection. In far-field detection, a microphone recording of the victim recording is played as a test segment on a telephone handset with a loudspeaker [15]. Meanwhile, cutting and pasting involves faking the sentence required by a text-dependent system [15]. This article will focus on Deepfake methods spoofing real voices rather than approaches that use edited recordings. This review will thus cover the detection methods used to identify synthetic and imitation Deepfakes, and replay-based attacks will be considered out of scope.