# Authenticity at Risk: Key Factors in the Generation and Detection of Audio Deepfakes +
## 1 Introduction  
In recent years, advances in machine learning (ML) technologies have led to the development of deepfakes, a technique that uses generative neural networks to create highly realistic fake multimedia content [1]. Deepfakes can manipulate images as well as video and audio, generating content that can deceive even trained observers. When combined with the Internet of Things (IoT), a wealth of new opportunities in uncountable applications such as personalized healthcare, manufacturing, transportation, smart homes, agriculture, or public safety is expected to impact our daily lives [2]. However, this combination also known as AIoT (Artificial Internet of Things), amplifies the potential risks posed by generative artificial intelligence, and in particular, of deepfakes: a vast network of cyberphysical devices (intelligent household gadgets, wearable devices including pulse sensors, accelerometers, microphones, and every other form of sensor) can serve as both a breeding ground for creating highly realistic deepfakes and a platform for their dissemination. Audio deepfakes (AD), in particular, represent a significant evolution in media manipulation technology. Synthetic voice generation, also known as voice cloning, imitationor spoofing, is conducted by using advanced algorithms that can imitate a person's voice with great precision, replicating not only pitch and timbre but also speech patterns and intonation. This technology has found applications in a variety of areas, from the entertainment industry to customer service automation. However, it has also opened the door to new types of fraud and manipulation, such as the creation of fake recordings that can be used to impersonate or commit scans. The risks associated with deepfakes are many and varied. In general, they can be used to spread disinformation, influence public opinion, and perpetrate financial fraud. Malicious actors could exploit AloT devices to collect data for generating deepfake content, while the interconnectedness of the network could facilitate rapid and widespread distribution of these fabricated media. This convergence of technologies exacerbates the challenges of detecting and mitigating the harmful impacts of deepfakes on individuals, organizations, and society as a whole [3] but even more on vulnerable persons, such as gender-based violence victims. In the context of cyberbullying, deepfakes can be particularly harmful, as they allow the creation and dissemination of false and compromising content that can damage the reputation of victims. Women, in particular, are a vulnerable group to such attacks, where audio deepfakes can be used to generate false recordings of an intimate or incriminating nature, exacerbating harassment and perpetuating gender-based violence in the digital sphere. This manipulative capacity not only affects privacy and personal safety but can also have serious psychological and social consequences for victims. On the other hand, the problem of detecting audio deepfakes or antispoofing can be regarded as the task of verifying the identity of a speaker, and therefore, belongs to the field of speaker recognition, a specific application of biometrics, i.e., the science of identifying individuals based on unique physical or behavioral characteristics [4]. Again, the new generative artificial intelligence technologies bring singularities and challenges to this task [5]. The motivation for this research lies in the development of systems capable of detecting risk situations where a victim could be impersonated. To this end, we have used recordings obtained through a system called _bindi_[6] designed to assess a woman's affective state through a set of intelligent wearable devices powered by ML, which capture her biosignals, including speech. However, in this paper, we will not be concerned with the modules regarding the affective state determination but only with the plausibility of generating credible audio deepfakes using voices recorded in similar circumstances.