每日跟讀#760: About Technology - What is Deepfake?
Deepfakes rely on a branch of AI called Generative Adversarial Networks (GANs). It requires two machine learning networks that teach each other with an ongoing feedback loop. The first one takes real content and alters it. Then, the second machine learning network, known as the discriminator, tests the authenticity of the changes.
「深度偽造」依賴人工智慧（AI）的分支「生成對抗網路」（GAN）。「生成對抗網路」責成兩個機器學習網路，透過不斷生成的回饋迴路互相學習。第一個「機器學習」網路變造真實的內容。然後，第二個機器學習網路「資料辨識器」，對這些更動進行鑑識。
GANs are still in the early stages, but people expect numerous potential commercial applications. For example, some can convert a single image into different poses. Others can suggest outfits similar to what a celebrity wears in a photo or turn a low-quality picture into a high-resolution snapshot.
「生成對抗網路」仍處於初步發展階段，但人們預期它在商業應用上擁有巨大潛力。例如，它可以將單張影像變換成不同的姿勢。它們可以找出名人在照片中所穿的類似服裝，或將低畫質的照片轉換為高解析度的快照。
But, outside of those helpful uses, deepfakes could have sinister purposes. Consider the blowback if a criminal creates a deepfake video of something that would hurt someone’s reputation — for instance, a deepfake video of a politician "admitting" to illegal activities, like accepting a bribe.
然而，除了正向的應用之外，也會有居心不良的「深度偽造」。倘若罪犯創造出一部妨害他人名聲的深度偽造影片，例如讓一名政治人物「承認」接受賄賂等違法行為的一部深偽影片，所造成的衝擊將難以衡量。
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Facebook is making its own AI deepfakes to head off a disinformation disaster 防制假訊息災難，臉書積極產製人工智慧「深度偽造」影片
Facebook fears that AI-generated “deepfake” videos could be the next big source of viral misinformation—spreading among its users with potentially catastrophic consequences for the next US presidential election.
臉書擔心用戶散播由人工智慧生成、未來有望成為假訊息主要來源的「深度偽造」影片，對下屆美國總統選舉帶來具有災難性潛力的後果。
Its solution? Making lots of deepfakes of its own, to help researchers build and refine detection tools.
提出因應「深度偽造」的解決方案？臉書自己產製大量深偽影片，助力研究者創建及改善辨識工具。
The rise of deepfakes has been driven by recent advances in machine learning. Algorithms capable of capturing and re-creating a person’s likeness have already been used to make point-and-click tools for pasting a person’s face onto someone else.
機器學習的技術進展助長了深偽的崛起。能夠捕捉及重現人物肖像的演算法，已被應用在點擊式工具的製作，它能把人臉貼在另一個人身上。
Facebook will dedicate $10 million. Together with Microsoft and academics from institutions including MIT, UC Berkeley, and Oxford University, the company is launching the Deepfake Detection Challenge, which will offer unspecified cash rewards for the best detection methods.
臉書將貢獻1000萬美元。與微軟及學術機構麻省理工學院、加州大學柏克萊分校、牛津大學等，一起推出「辨識深偽挑戰賽」，該挑戰賽將頒發未指定金額的獎金給最優秀的檢測方法。
Source article: https://features.ltn.com.tw/english/article/paper/1327942 ; https://features.ltn.com.tw/english/article/paper/1329588