Detecting Audio Deepfakes: Integrating CNN and BiLSTM with Multi-Feature Concatenation-Reference-Cited by-同舟云学术

Detecting Audio Deepfakes: Integrating CNN and BiLSTM with Multi-Feature Concatenation

Published:2024-06-24 Issue: Volume: Page:271-276
ISSN:
Container-title:Proceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security
language:
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Author:

Wani Taiba Majid¹^ORCID,Qadri Syed Asif Ahmad²^ORCID,Comminiello Danilo¹^ORCID,Amerini Irene¹^ORCID

Affiliation:

1. Sapienza University of Rome, Rome, Italy

2. National Tsing Hua University, Hsinchu, Taiwan

Funder

SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union ? NextGenerationEU and Sapienza University of Rome project 2022?2024 ?EV2?

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3658664.3659647

Reference23 articles.

1. Jahangir Alam and Patrick Kenny. 2017. Spoofing detection employing infinite impulse response-constant q transform-based feature representations. In 2017 25Th european signal processing conference (EUSIPCO). IEEE, 101--105.

2. Dora M Ballesteros Yohanna Rodriguez and Diego Renza. 2020. A dataset of histograms of original and fake voice recordings (h-voice). Data in brief 29.

3. Nidhi Chakravarty and Mohit Dua. 2024. A lightweight feature extraction technique for deepfake audio detection. Multimedia Tools and Applications 1--25.

4. Deepfake Audio Detection via MFCC Features Using Machine Learning

5. Lian Huang and Jinhong Zhao. 2021. Audio replay spoofing attack detection using deep learning feature and long-short-term memory recurrent neural network. In AIIPCC 2021; The Second International Conference on Artificial Intelligence, Information Processing and Cloud Computing. VDE, 1--5.