Exploring Deepfakes: Deploy powerful AI techniques for face replacement and more with this comprehensive guide
Lyon, Bryan, Tora, Matt
- 出版商: Packt Publishing
- 出版日期: 2023-03-28
- 售價: $1,680
- 貴賓價: 9.5 折 $1,596
- 語言: 英文
- 頁數: 192
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1801810699
- ISBN-13: 9781801810692
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相關分類:
人工智慧
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相關主題
商品描述
Master the innovative world of deepfakes and generative AI for face replacement with this full-color guide
Key Features
• Understand what deepfakes are, their history, and how to use the technology ethically
• Get well-versed with the workflow and processes involved to create your own deepfakes
• Learn how to apply the lessons and techniques of deepfakes to your own problems
Book Description
Applying Deepfakes will allow you to tackle a wide range of scenarios creatively.
Learning from experienced authors will help you to intuitively understand what is going on inside the model. You'll learn what deepfakes are and what makes them different from other machine learning techniques, and understand the entire process from beginning to end, from finding faces to preparing them, training the model, and performing the final swap.
We'll discuss various uses for face replacement before we begin building our own pipeline. Spending some extra time thinking about how you collect your input data can make a huge difference to the quality of the final video. We look at the importance of this data and guide you with simple concepts to understand what your data needs to really be successful.
No discussion of deepfakes can avoid discussing the controversial, unethical uses for which the technology initially became known. We'll go over some potential issues, and talk about the value that deepfakes can bring to a variety of educational and artistic use cases, from video game avatars to filmmaking.
By the end of the book, you'll understand what deepfakes are, how they work at a fundamental level, and how to apply those techniques to your own needs.
What you will learn
• Gain a clear understanding of deepfakes and their creation
• Understand the risks of deepfakes and how to mitigate them
• Collect efficient data to create successful deepfakes
• Get familiar with the deepfakes workflow and its steps
• Explore the application of deepfakes methods to your own generative needs
• Improve results by augmenting data and avoiding overtraining
• Examine the future of deepfakes and other generative AIs
• Use generative AIs to increase video content resolution
Who this book is for
This book is for AI developers, data scientists, and anyone looking to learn more about deepfakes or techniques and technologies from Deepfakes to help them generate new image data. Working knowledge of Python programming language and basic familiarity with OpenCV, Pillow, Pytorch, or Tensorflow is recommended to get the most out of the book.
商品描述(中文翻譯)
掌握深度偽造和生成式人工智慧(AI)進行臉部替換的創新世界,這本全彩指南將幫助您。
主要特點:
- 了解深度偽造的定義、歷史以及如何在道德上使用這項技術。
- 熟悉創建自己的深度偽造所涉及的工作流程和過程。
- 學習如何將深度偽造的教訓和技巧應用於解決自己的問題。
書籍描述:
應用深度偽造將使您能夠創造出各種創意場景。
從經驗豐富的作者那裡學習將幫助您直觀地理解模型內部的運作。您將了解深度偽造的定義以及它與其他機器學習技術的區別,並從頭到尾了解整個過程,從尋找臉部到準備臉部,訓練模型,以及進行最終替換。
在開始構建自己的流程之前,我們將討論臉部替換的各種用途。花一些額外的時間思考如何收集輸入數據對最終視頻的質量會有很大的影響。我們將探討數據的重要性,並通過簡單的概念指導您了解數據需要什麼才能真正成功。
在討論深度偽造時,無法避免討論這項技術最初因其具有爭議性和不道德用途而聞名。我們將討論一些潛在問題,並談論深度偽造在各種教育和藝術用例中的價值,從視頻遊戲角色到電影製作。
通過閱讀本書,您將了解深度偽造的定義、基本運作原理以及如何將這些技術應用於自己的需求。
您將學到:
- 清楚了解深度偽造及其創建過程。
- 了解深度偽造的風險以及如何減輕風險。
- 收集高效數據以創建成功的深度偽造。
- 熟悉深度偽造的工作流程和步驟。
- 探索將深度偽造方法應用於自己的生成需求。
- 通過增加數據和避免過度訓練來改善結果。
- 檢視深度偽造和其他生成式人工智慧的未來。
- 使用生成式人工智慧提高視頻內容的解析度。
本書適合人工智慧開發人員、數據科學家以及任何希望了解更多關於深度偽造或從深度偽造到幫助他們生成新圖像數據的技術和技術的人。建議具備Python編程語言的工作知識,並對OpenCV、Pillow、Pytorch或Tensorflow有基本的熟悉,以充分利用本書。
目錄大綱
1. Surveying Deepfakes
2. Examining Deepfake Ethics and Dangersand Ethics
3. Acquiring and Processing Data
4. The Deepfake workflow
5. Extracting faces from the video
6. Training a Deepfake Model
7. Swapping the Face Back into the Video
8. Applying the lessons of Deepfakes
9. The future of Deepfakes
目錄大綱(中文翻譯)
1. 深度偽造調查
2. 深度偽造的倫理和危險性
3. 獲取和處理數據
4. 深度偽造的工作流程
5. 從視頻中提取人臉
6. 訓練深度偽造模型
7. 將臉部重新插入視頻中
8. 應用深度偽造的教訓
9. 深度偽造的未來