Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Giri, Sanjula; Sahu, Anusmita; Maurya, Ankur; Sinha, Ambrisha; Sharma, Vandana; Kedar, Tilottama Title A name given to the resource Deep-fake Detection for Recognising Altered Audio using Deep Learning Approach Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Artificial Intelligence and Machine Vision, AIMV 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AIMV66517.2025.11203307" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIMV66517.2025.11203307</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021833320?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021833320?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 &amp; 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 Description An account of the resource 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. Subject The topic of the resource audio detection; deep-fake; machine learning models; signal-processing; speaker-recognition; technology; voice-activity detection Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152697-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online