Deepfake Defense: Constructing and Evaluating a Specialized U rdu Deepfake Audio Dataset (2024)

Abstract: Deepfakes, particularly in the auditory domain, have become a significant threat, necessitating the development of robust countermeasures. This paper addresses the escalating challenges posed by deepfake attacks on Automatic Speaker Verification (ASV) systems. We present a novel Urdu deepfake audio dataset for deepfake detection, focusing on two spoofing attacks – Tacotron and VITS TTS. The dataset construction involves careful consideration of phonemic cover and balance and comparison with existing corpora like PRUS and PronouncUR. Evaluation with AASIST-L model shows EERs of 0.495 and 0.524 for VITS TTS and Tacotron-generated audios, respectively, with variability across speakers. Further, this research implements a detailed human evaluation, incorporating a user study to gauge whether people are able to discern deepfake audios from real (bonafide) audios. The ROC curve analysis shows an area under the curve (AUC) of 0.63, indicating that individuals demonstrate a limited ability to detect deepfakes (approximately 1 in 3 fake audio samples are regarded as real). Our work contributes a valuable resource for training deepfake detection models in low-resource languages like Urdu, addressing the critical gap in existing datasets. The dataset is publicly available at: https://github.com/CSALT-LUMS/urdu-deepfake-dataset.

## introduction automatic speaker verification, a method for biometric person recognition, has gained popularity in recent years. however, this surge in popularity has also given rise to new challenges in the form of spoofing or deepfake attacks. initially coined on reddit in 2017, the term 'deepfake' @xcite denotes the application of deep learning techniques for face swapping in videos. presently, the term has evolved to broadly encompass any audio or video manipulation where key attributes are digitally altered or swapped using artificial intelligence (ai) technologies. the asvspoof community classifies these attacks into two main categories: logical access, involving deepfake-generated audios, speech synthesis, and voice conversion, and physical access, which includes replay attacks and impersonation @xcite . deepfakes, a complex way of manipulating media, make fake content easier to generate and harder to detect. speech synthesis models now allow the creation of deepfakes that are undetectable by the human ear or even verification systems @xcite . in 2019, impostors leveraged ai-driven software to replicate the voice of a corporate executive, orchestrating a fraudulent transfer of usd 243,000 @xcite . this incident underscores the imperative of developing robust methods to accurately identify deepfake audio in order to counteract such fraudulent activities. in a behavioral study, @xcite revealed that people cannot easily detect deepfakes, yet they perceive that they can. thus, these fake audios have the potential to spread misinformation, create mass panic and havoc, malign personalities, and change narratives. moreover, beyond this social impact, deepfakes have the power to break through systems protected by voice recognition through the spoofing attacks listed above. considering the adverse effects of deepfake audios, it is crucial to develop systems capable of discerning between real and deepfake audio. the asvspoof challenge, a community-led initiative, promotes the development of such countermeasures against deepfakes and audio spoofing @xcite @xcite . countermeasures against deepfakes include detection algorithms designed to identify features in deepfake audios. the physical attributes of sound, encompassing pitch, texture, loudness, and duration, can now be accurately replicated in artificially generated deepfake audios. to detect the features that differentiate bonafide (actual utter-ances of the people) and fake audios, the model needs to train on a large amount of data @xcite . these differentiations are based on spectral and temporal differences and micro features @xcite @xcite . widely used datasets created for this purpose include wavefake (frank and schönherr, 2021), fakeavceleb @xcite , and the asvspoof dataset @xcite itself. these datasets, from high-resource languages, exemplify the large amount of data required to train deepfake detection models. unfortunately, in lowresource languages, this large amount of data is unavailable. to cater to this lack of data in urdu, we create and evaluate a dataset that can be used to train against spoofing attacks. ## methodology to create the text corpus for the dataset, we randomly select sentences from reputable urdu news sources. we then analyze the phonemic structure of the text corpus, ensuring its alignment with natural language patterns. statistical measures confirm the dataset's phonemic cover and balance. for the spoofing attacks, advanced text-to-speech models tacotron and vits tts are utilized to generate deepfake audios. figure 2 highlights the steps taken in dataset construction. ## limitations and conclusion in presenting our urdu deepfake detection dataset, we recognize limitations and suggest areas for future improvement. the dataset currently emphasizes two text-to-speech (tts) synthesis methods-tacotron and vits tts. expanding to a broader range of tts techniques in future iterations will enhance deepfake detection. the dataset's reliance on a convenience sample leads to a gender imbalance in the speakers, highlighting the need for a more diverse dataset in future work. additionally, our dataset primarily covers logical access scenarios; future research could include physical access scenarios for added detection challenges. in conclusion, our dataset lays a solid foundation for deepfake detection research in the urdu language. addressing the outlined limitations and pursuing future research directions will further enhance the dataset's value and contribute to the advancement of deepfake detection technologies in low-resource languages.