Reenactment的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到附近那裡買和營業時間的推薦產品

The Hunger of the Gods

為了解決Reenactment的問題，作者Gwynne, John 這樣論述：

John Gwynne studied and lectured at Brighton University. He’s played double bass in a rock ’n’ roll band and traveled the USA and Canada. He is married with four children and lives in Eastbourne, where he is part of a Viking reenactment group. When not writing, he can often be found standing in a sh

ield wall with his three sons about him. His dogs think he is their slave.

Reenactment進入發燒排行的影片

深度偽造語音之辨識檢測

為了解決Reenactment的問題，作者吳靜瑜 這樣論述：

摘要 iAbstract ii目錄 iii表目錄 v圖目錄 vi壹、 緒論 11.1 研究背景 11.2 研究動機 21.3 研究目的 31.4 研究架構 4貳、 文獻探討 52.1 人工智慧（Artificial intelligence） 52.1.1 機器學習（Machine Learning） 52.1.2 深度學習（Deep Learning） 52.2 語音識別 62.2.1 語音識別流程 62.2.2 聲學特徵 72.2.3 線性預估倒頻譜係數（LPCC） 72.2.4 梅爾頻率倒譜係數（MFCCs） 82.2.

5 MFCC計算步驟 92.3 語者驗證 122.4 x-vector 122.5 相關研究 132.5.1 變聲器原理 132.5.2 語音合成 142.5.3 Clone voice 152.5.4 深度偽造技術 162.5.5 深度偽造技術介紹 172.5.6 深度偽造技術應用 192.5.7 深度偽造技術現況 19參、 研究方法 223.1 研究架構 223.2 系統模組化 233.2.1 語音獲取與實驗設備 253.3 實驗流程 253.4 辨識系統 263.4.1 資料集介紹 283.4.2 預處理 293

.4.3 特徵擷取 293.4.4 X-vector 303.4.5 模型評估 333.4.6 激活函數 343.5 聲紋系統 353.5.1 資料集介紹 353.5.2 特徵擷取 363.5.3 GMM 403.5.4 語者註冊與驗證 403.5.5 模型評估 41肆、 實驗結果 424.1 辨識系統實驗結果 424.2 聲紋系統實驗結果 43伍、 結論 525.1 結論 525.2 研究限制及未來展望 52參考文獻 53

Re-Living the American Frontier: Western Fandoms, Reenactment, and Historical Hobbyists in Germany and America Since 1900

為了解決Reenactment的問題，作者Reagin, Nancy 這樣論述：

Nancy Reagin is professor of European history and gender studies at Pace University in New York City. She is author of A German Women’s Movement: Class and Gender in Hanover, 1880-1933. She lives in Montclair, New Jersey.

Facestamp: Self-Reference Proactive Deepfake Detection using Facial Attribute Deep Watermarking

為了解決Reenactment的問題，作者Joshua C. Manzano 這樣論述：

Deepfakes are progressively harder to distinguish and present a growingproblem to image authenticity in society. Existing studies that focus ondeepfake detection rely on artifacts or flaws generated by the deepfakeprocess which may not be present in novel deepfake models. This necessitates a proact

ive approach that is more robust and generalizable. Recentworks on proactive defense rely on deep watermarking, where they embed a Unique Identification (UID) to an image. To verify authenticity, atrusted authority needs to decode its hidden UID and cross-reference it to acentralized dataset contain

ing all existing UIDs. Overall, its reliance on atrusted centralized authority that stores individual UIDs makes it inflexibleand impedes its widespread adoption. Moreover, this authentication approach has constrained effectiveness when the number of users is limited.In this paper, we present Facest

amp, a deep watermarking model for a self-reference proactive defense against deepfakes. We address this problemby directly embedding facial attributes, instead of a UID, to an image using deep watermarking. Image-derived attributes such as facial attributesverify the legitimacy of the image through

the identification of inconsistencies between the decoded attributes and current attributes present in theimage. This eliminates the need for a centralized verification process andenables independent verification. In our experiments, we show that Facestamp allows the recovery of facial attributes i

n the wild and the subsequentverification of the current face to determine the legitimacy of the givenimage. Facestamp is able to defend against deepfakes across three deepfake models, showing promising performance in two popular datasets andis more robust to common post-processing image operations

compared to existing methods.