All-for-One and One-For-All: Deep learning-based feature fusion for Synthetic Speech Detection

Autor:	Mari, Daniele, Salvi, Davide, Bestagini, Paolo, Milani, Simone
Rok vydání:	2023
Předmět:	Computer Science - Sound Computer Science - Computation and Language Computer Science - Cryptography and Security Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	Recent advances in deep learning and computer vision have made the synthesis and counterfeiting of multimedia content more accessible than ever, leading to possible threats and dangers from malicious users. In the audio field, we are witnessing the growth of speech deepfake generation techniques, which solicit the development of synthetic speech detection algorithms to counter possible mischievous uses such as frauds or identity thefts. In this paper, we consider three different feature sets proposed in the literature for the synthetic speech detection task and present a model that fuses them, achieving overall better performances with respect to the state-of-the-art solutions. The system was tested on different scenarios and datasets to prove its robustness to anti-forensic attacks and its generalization capabilities. Comment: Accepted at ECML-PKDD 2023 Workshop "Deep Learning and Multimedia Forensics. Combating fake media and misinformation"
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.15555 Zobrazit plný text záznamu View this record from Arxiv