## 2 State of the Art
### Detection of Audio Fakes: A Technological Challenge  
Within the technological field, fake audios have been studied for decades. Various initiatives and competitions, such as the ASVspoof Challenge or Audio Deepfake Detection (ADD), have promoted the development and evaluation of solutions to address voice spoofing [34; 35]. In them, a challenge is posed to the participating research groups who compare the effectiveness of their solutions on a common set of data. ASVspoof focuses on voice spoofing detection, while ADD specifically addresses audio deepfakes [36]. The challenges posed by these issues have varied over time and reflect the main challenges or concerns of the moment regarding audio synthesis. Thus, the first challenges focused on \"audio spoofing\", with which attackers attempt to deceive biometric authentication systems by generating audio that imitates the voice of a person authorized to access a system, which has serious implications in terms of security and privacy. The ASVspoof 2015 antispoofing challenge pioneered the introduction of databases containing examples of TTS (Text-to-Speech) and VC (Voice Conversion) attacks, establishing an initial benchmark for detection techniques [37]. On the other hand, replay attacks were also discussed (replay attacks), which simulate a more practical and common scenario wherein an attacker plays an authentic voice recording (ASVspoof 2017). This posed new challenges in terms of detection, as the reproduced audios can be of high quality and more difficult to differentiate from the original recordings than those obtained from synthesis or conversion [38]. Subsequently, tests were introduced in both logical access (LA; speech synthesis/conversion attacks) and physical access (PA; replay attacks) scenarios. With the participation of 63 research teams, ASVspoof 2019 marked significant progress in protecting against fake audio threats and supplantation [39]. With the development of AI and deep audio synthesis, the challenges have changed. ASVspoof 2021 was the first edition that did not provide training data, thus reflecting the more realistic conditions wherein spoofing attacks and deepfakes cannot be predicted with certainty [34]. In 2022, the specific challenge for detecting audio deepfakes, ADD, also appeared. ADD2022 stood out for its focus on completely and partially fake audio under realistic conditions, such as noise and background music, as well as the generation of audio that fools detection models [36]. In ADD2023, unlike its previous edition, it was not only focused on the binary classification of audios as real or fake, but also tried to locate manipulated segments within partially fake audios and the identification of detection techniques [35]. These competitions promote innovation and encourage the creation of new audio deepfake detection methods, provide benchmarks for an objective and standardized evaluation of different approaches, and encourage collaboration within the scientific community. By offering common datasets and metrics, they facilitate direct comparison of these tools and highlight the relevance of continued research in this field. For this reason, the results of these challenges are a good indicator of which are the most cutting-edge techniques of the state of the art. There are several techniques that were presented as part of the challenges and that have subsequently been adopted with good results in the academic or industrial field. For example, ASSERT, developed by the Johns Hopkins Whiting School of Engineering, combines compression and residual networks to detect fake audio with high precision [40]. Similarly, Attentive Filtering Networks incorporates attentive filtering techniques in combination with residual networks to improve the detection of replay attacks, achieving an error rate of 8.99% in ASVspoof 2017 [41].