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Deepfakes, synthetic multimedia files generated by artificial intelligence, are drastically undermining digital credibility. Their ability to manipulate our perception of reality has created a new and complex battleground for disinformation, posing a critical threat to non-English-speaking audio with distinctive accents. Consequently, the objective of this study is to determine the human capacity to detect deepfake audio in Spanish with a Paraguayan accent through an experiment conducted with an Android application called ReFake (developed specifically for this research). In this experiment, 450 participants, aged 16–72, evaluated 10 audio samples of up to 15 s each, classifying them as authentic (belonging to Paraguayan journalists) or fake (generated with ElevenLabs). The findings suggests that human ear is more accurate than artificial intelligence (AI) at detecting vocal ‘naturalness’. This ability is influenced by generational age and educational level, with younger people and those with postgraduate degrees demonstrating greater performance. Conversely, gender and nationality do not influence detection, although the high prosodic quality of deepfakes still leads to errors in human judgment. Given these results, it is crucial to adapt and develop new strategies for a secure and resilient online ecosystem. Full article

This study examined how second language (L2) speakers’ individual differences in music perception abilities, singing abilities and phonetic aptitude relate to their L2 phonological awareness. To measure participants’ L2 phonological awareness, we used an accent faking paradigm, where participants were asked to speak in their native language (German) while imitating a strong L2 accent (English). We measured their musical abilities with the AMMA test and their singing abilities with two singing tasks and a self-report questionnaire. Their phonetic aptitude was assessed with a combination of phonological short-term memory tasks (forward and backward digit span tasks), and language perception and production tasks, in which participants needed to process and imitate sounds from unfamiliar languages. A regression analysis revealed that singing abilities and phonetic aptitude could predict participants’ English faking abilities. This suggests that being able to sing could help learners produce and memorise highly accurate L2 sounds, although their performance could also partly be explained by innate learning capacities such as phonetic aptitude. This study also proposes a new combination of tests to obtain a well-rounded assessment of individual differences in phonetic aptitude. Full article

A number of AI-generated tools are used today to clone human voices, leading to a new technology known as Audio Deepfakes (ADs). Despite being introduced to enhance human lives as audiobooks, ADs have been used to disrupt public safety. ADs have thus recently come to the attention of researchers, with Machine Learning (ML) and Deep Learning (DL) methods being developed to detect them. In this article, a review of existing AD detection methods was conducted, along with a comparative description of the available faked audio datasets. The article introduces types of AD attacks and then outlines and analyzes the detection methods and datasets for imitation- and synthetic-based Deepfakes. To the best of the authors’ knowledge, this is the first review targeting imitated and synthetically generated audio detection methods. The similarities and differences of AD detection methods are summarized by providing a quantitative comparison that finds that the method type affects the performance more than the audio features themselves, in which a substantial tradeoff between the accuracy and scalability exists. Moreover, at the end of this article, the potential research directions and challenges of Deepfake detection methods are discussed to discover that, even though AD detection is an active area of research, further research is still needed to address the existing gaps. This article can be a starting point for researchers to understand the current state of the AD literature and investigate more robust detection models that can detect fakeness even if the target audio contains accented voices or real-world noises. Full article

To keep its business reliable, Google is concerned to ensure the quality of apps on the store. One crucial aspect concerning quality is security. Security is achieved through Google Play protect and anti-malware solutions. However, they are not totally efficient since they rely on application features and application execution threads. Google provides additional elements to enable consumers to collectively evaluate applications providing their experiences via reviews or showing their satisfaction through rating. The latter is more informal and hides details of rating whereas the former is textually expressive but requires further processing to understand opinions behind it. Literature lacks approaches which mine reviews through sentiment analysis to extract useful information to improve the security aspects of provided applications. This work goes in this direction and in a fine-grained way, investigates in terms of confidentiality, integrity, availability, and authentication (CIAA). While assuming that reviews are reliable and not fake, the proposed approach determines review polarities based on CIAA-related keywords. We rely on the popular classifier Naive Bayes to classify reviews into positive, negative, and neutral sentiment. We then provide an aggregation model to fusion different polarities to obtain application global and CIAA reputations. Quantitative experiments have been conducted on 13 applications including e-banking, live messaging and anti-malware apps with a total of 1050 security-related reviews and 7,835,322 functionality-related reviews. Results show that 23% of applications (03 apps) have a reputation greater than 0.5 with an accent on integrity, authentication, and availability, while the remaining 77% has a polarity under 0.5. Developers should make a lot of effort in security while developing codes and that more efforts should be made to improve confidentiality reputation. Results also show that applications with good functionality-related reputation generally offer a bad security-related reputation. This situation means that even if the number of security reviews is low, it does not mean that the security aspect is not a consumer preoccupation. Unlike, developers put much more time to test whether applications work without errors even if they include possible security vulnerabilities. A quantitative comparison against well-known rating systems reveals the effectiveness and robustness of CIAA-RepDroid to repute apps in terms of security. CIAA-RepDroid can be associated with existing rating solutions to recommend developers exact CIAA aspects to improve within source codes. Full article