Deepfake Defense: Constructing and Evaluating a Specialized Urdu Deepfake Audio Dataset
Sheza Munir, Wassay Sajjad, Mukeet Raza, Emaan Mujahid Abbas, Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
2023
Discernment, Detection, Dataset, Urdu
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.
Evaluating Deepfake Speech and ASV Systems on African Accents
Kweku Andoh Yamoah, Hussein Baba Fuseini, David Ebo Adjepon-Yamoah, Dennis Asamoah Owusu
2023
Generation, Detection, English
This work hypothesizes whether ASV systems can be fooled by deepfake speech generated on African accents. Prior studies primarily concentrated on native English speakers. This research centers on African English speakers who frequently interact with digital systems. Experiments assessed a selected DNN-based deepfake audio system and an ASV system, demonstrating that ASV systems are less susceptible to deepfake audio deception in African accents.
Faked Speech Detection with Zero Prior Knowledge
Sahar Al Ajmi, Khizar Hayat, Alaa M. Al Obaidi, Naresh Kumar, Munaf Najmuldeen
2024
Detection, Dataset, English, Arabic, Multiple Languages
This work introduces a neural network method to develop a classifier that will blindly classify an input audio as real or mimicked; the word ’blindly’ refers to the ability to detect mimicked audio without references or real sources.
Generation and Detection of Sign Language Deepfakes – A Linguistic and Visual Analysis
Shahzeb Nacem, Muhammad Riyyan Khan, Usman Tariq, Abhinav Dhall, Carlos Ivan Colon, Hasan Al-Nashash
2024
Generation, Discernment, Detection, Dataset, Accessibility, American Sign Language
This research presents a positive application of deepfake technology in upper body generation, while performing sign-language for the Deaf and Hard of Hearing (DHoH) community.
Voice Conversion and Spoofed Voice Detection from Parallel English and Urdu Corpus using Cyclic GANs
Summra Saleem; Aniqa Dilawari; Muhammad Usman Ghani Khan; Muhammad Husnain
2019
Generation, Detection, English, Urdu
This study addresses the threat of identity theft in automatic speech verification systems using a Cyclic GAN-based model to generate and detect spoofed voices, specifically focusing on Urdu and English speech datasets. By leveraging adversarial examples for spoof detection and using Gradient Boosting to differentiate real from fake voices, the approach shows promise but requires further data and refinement for practical large-scale implementation.