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現代商用英文(四版)

為了解決9023的問題，作者黃瑪莉 這樣論述：

　　範例全都取材自實務工作上的各種商用英文信函，按照進出口流程順序編排，方便讀者查閱參考。 　　新版增加了許多和證照考試相關的資料，例如:NEW TOEIC文法選擇試題範例、國貿大會考歷屆英文考題範例、 國貿業務丙級基礎貿易英文題庫及範例等。 　　除進出口業務所需用到的所有相關文件外，也增加了最新的：煙燻證明、原廠證明書、校正證明書、兩岸三地產證（ECFA用）、授權書、經銷合約、稽核報告書、ECFA整體經濟效益∕ECFA項目及進口稅率、INCOTERMS 2010最新版本內容及應用、台灣目前外銷的高科技產品英文名稱及考試常出現的國貿縮寫字等資料。 　　增加和大陸簽約時訂單上

常見到的「一般訂單條款」的內容；及信用狀上常見的文件要求及額外條款。 　　內容完全以中英文對照方式呈現，文辭簡潔易懂，內容豐富完整，排版清晰易讀，考題、範例及練習最多，學習輕鬆，進步迅速，方便應用於實務工作及相關證照考試上，為現代商用英文最佳學習範本及國貿業界必備參考工具書。 　　完全適用於學校的授課老師、學習商用英文的學生、進出口貿易業務從業人員、以及對英文有興趣的自修學習者。 本書特色 　　本書附贈 國際貿易實用參考附表    

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

為了解決9023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.