With the improvement of deep fake technology, the problem of detecting fabricated audio has become significant. Modern approaches have certain shortcomings that do not allow their implementation in existing vehicles. The article is devoted to the creation of a unique approach to the analysis of audio data to detect the fact of contextual falsification of information. The proposed solution is based on a complex combination of recurrent and convolutional neural networks. The results of the research conducted on a self-created data set related to operational information dedicated to Russia’s invasion of Ukraine, and a comparison with existing approaches, assert the high efficiency of the proposed solution and the possibility of its further implementation as part of an unmanned systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Using RCNN to Indentify the Fake Audio Information


    Contributors:


    Publication date :

    2024-10-22


    Size :

    2363194 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Lane Tracking with Deep Learning: Mask RCNN and Faster RCNN

    Ortatas, Fatma Nur / Cetin, Emrah | IEEE | 2022


    Preprocessed Faster RCNN for Vehicle Detection

    Manana, Mduduzi / Tu, Chunling / Owolawi, Pius Adewale | IEEE | 2018


    Improved Faster RCNN for Traffic Sign Detection*

    Wang, Fei / Li, Yidong / Wei, Yunchao et al. | IEEE | 2020


    Vehicle type classification and attribute prediction using multi-task RCNN

    Huo, Zhuoqun / Xia, Yizhang / Zhang, Bailing | IEEE | 2016


    A Faster RCNN-Based Pedestrian Detection System

    Zhao, Xiaotong / Li, Wei / Zhang, Yifang et al. | IEEE | 2016