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IoT based application designing of Deep Fake Test for Face animation

Published: 18 October 2022 Publication History

Abstract

Development of Deep Learning models of Internet of Things (IoT) enclosures with limited resources are difficult because Both Quality of Results are difficult to achieve - QoR as follows two models, DNN Model, and Inference Accuracy and Quality of Services such as power consumption, throughput, and latency. Currently, the development of DNN models is often separated from deploying them to IoT devices, which leads to the most effective solution. If there are many records that represent objects of substantially the same class (face, human body, etc.), you can apply frames to each object of this class. To achieve this, use an independent representation to distinguish between appearance and progress data. Deep fake detection is achieved by using a novel, lightweight Deep Learning method on the IoT platform that is memory-efficient and lightweight. It is carried out in two different stages. The first phase of the deep fake test aims to implement a method of extracting images from a video and using them in conjunction with a Deep Neural Network to implement a test for face animation. It has been reported that the impact of the background elimination has been reported before the background subtraction. Here the Trans GAN model is used for the image classification. In the second phase, the work can be recorded and executed by the IOT device that can record live video streams and then detect activity involved in live video. An activity detection prototype based on IoT devices with small processing power is presented. This prototype provides improvements to the system, extending its application in various ways to improve portability, networking, and other equipment capabilities. The proposed architecture will be evaluated against four highly competitive object detection benchmarking tasks CIFAR10, CIFAR100, SVHN, and ImageNet.

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Cited By

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  • (2024)Comprehensive Analysis of Attacks and Defenses in IoT Sensory Big Data AnalysisTechnological Advancements in Data Processing for Next Generation Intelligent Systems10.4018/979-8-3693-0968-1.ch002(24-57)Online publication date: 18-Mar-2024
  • (2024)Boosting Deep Feature Fusion-Based Detection Model for Fake Faces Generated by Generative Adversarial Networks for Consumer Space EnvironmentIEEE Access10.1109/ACCESS.2024.347012812(147680-147693)Online publication date: 2024
  • (2023)A Survey on AI-Enabled Attacks and AI-Empowered Countermeasures in Physical Layer2023 IEEE 9th World Forum on Internet of Things (WF-IoT)10.1109/WF-IoT58464.2023.10539554(1-7)Online publication date: 12-Oct-2023

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    cover image ACM Other conferences
    ICCBDC '22: Proceedings of the 2022 6th International Conference on Cloud and Big Data Computing
    August 2022
    88 pages
    ISBN:9781450396578
    DOI:10.1145/3555962
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 18 October 2022

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    Author Tags

    1. Deep Fake
    2. Face animation
    3. GAN
    4. Object detection

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    View all
    • (2024)Comprehensive Analysis of Attacks and Defenses in IoT Sensory Big Data AnalysisTechnological Advancements in Data Processing for Next Generation Intelligent Systems10.4018/979-8-3693-0968-1.ch002(24-57)Online publication date: 18-Mar-2024
    • (2024)Boosting Deep Feature Fusion-Based Detection Model for Fake Faces Generated by Generative Adversarial Networks for Consumer Space EnvironmentIEEE Access10.1109/ACCESS.2024.347012812(147680-147693)Online publication date: 2024
    • (2023)A Survey on AI-Enabled Attacks and AI-Empowered Countermeasures in Physical Layer2023 IEEE 9th World Forum on Internet of Things (WF-IoT)10.1109/WF-IoT58464.2023.10539554(1-7)Online publication date: 12-Oct-2023

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