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    政大典藏 > College of Commerce > Department of MIS > Theses >  Item 140.119/149467


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/149467


    Title: 深度偽造的投毒防禦技術研究
    Poison Defense on Deepfake Attacks
    Authors: 郭丞堯
    Guo, Cheng-Yao
    Contributors: 郁方
    Yu, Fang
    郭丞堯
    Guo, Cheng-Yao
    Keywords: 毒藥防禦
    深度偽造
    對抗例攻擊
    Poison defense
    Deepfake
    Adversarial attack
    Date: 2024
    Issue Date: 2024-02-01 10:56:20 (UTC+8)
    Abstract: 深度偽造技術的應用已趨於成熟，並在網路上廣泛傳播。這項技術的濫用會導致人們的隱私與安全受到侵害。為應對這一威脅，我們引入了一種新的防禦策略，稱為“糖衣毒藥”。該方法涉及在生成模型的學習過程中有策略地擾動潛在向量。通過設計，我們的方法旨在將視覺效果視為“毒藥”，引發故意的干擾，同時最小化重建損失充當“糖衣”，誤導深度偽造模型。這種雙重目的的策略能有效地抵禦深度偽造攻擊，並提高被檢測率。我們提出了一種系統性的機制，來生成影片補丁，以減輕與深度偽造相關的風險，並在特定的深度偽造人臉交換應用背景下實施和驗證我們的方法。
    The application of deepfake face-swapping technology has matured and become widespread on the internet, posing a significant threat to application security and privacy. In response to this growing concern, we introduce a novel defense strategy named "sugar-coated poison." This approach involves strategically perturbing latent vectors during the learning process of generative models. By design, our method aims to treat visual effects as the "poison," inducing intentional disruptions, while minimizing reconstruction loss acts as the "sugar," misleading the deepfake model. This dual-purpose strategy effectively defends against deepfake attacks. We present a systematic mechanism for generating video patches to mitigate the risks associated with deepfake manipulation, implementing and validating our approach specifically in the context of deepfake face-swapping applications.
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    Description: 碩士
    國立政治大學
    資訊管理學系
    110356024
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110356024
    Data Type: thesis
    Appears in Collections:[Department of MIS] Theses

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