ItemConference Paper

Deep-fake Detection for Recognising Altered Audio using Deep Learning Approach

Title
Deep-fake Detection for Recognising Altered Audio using Deep Learning Approach
Creator
Giri, Sanjula; Sahu, Anusmita; Maurya, Ankur; Sinha, Ambrisha; Sharma, Vandana; Kedar, Tilottama
Description
Ensuring the validity of audio recordings is becoming increasingly difficult due to deep-fake technology. Audio-analysis is used to identify deep-fake audio, which has been examined here. Machine-learning models can be made technological to compare between real and modified audio by examining minute artifacts and inconsistencies added to during the deep-fake production process. In this work, advanced signal-processing techniques like spectrum-analysis, voice-activity detection, and speaker-recognition; are used to extract relevant information from audio recordings. In order to exact deep-fake audio detection, these features are then utilized to guide and judge deep-learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The objective is to create reliable and efficient techniques for detecting altered audio, almost eliminating the possible dangers. The goal is to provide reliable and efficient techniques for detecting modified audio in order to mitigate the possible risks related to deep-fake technology in a number of fields, such as social-media, journalism, and security. 2025 IEEE.
Source
2025 International Conference on Artificial Intelligence and Machine Vision, AIMV 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
audio detection; deep-fake; machine learning models; signal-processing; speaker-recognition; technology; voice-activity detection
Coverage
Giri S., Deemed to Be University, School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sahu A., Deemed to Be University, School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Maurya A., Bennett University, School of Computer Science Engineering & Technology, India; Sinha A., Galgotias University, School of Education, Greater Noida, India; Sharma V., CHRIST University, Computer Science Department, Bengaluru, India; Kedar T., Deemed to Be University, School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152697-9;
Format
online
Language
English
Type
Conference paper

Citation

Giri, Sanjula; Sahu, Anusmita; Maurya, Ankur; Sinha, Ambrisha; Sharma, Vandana; Kedar, Tilottama, “Deep-fake Detection for Recognising Altered Audio using Deep Learning Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25744.

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