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1.媒體來源: Ars Technica UK

2.完整新聞標題:
Google AI invents its own cryptographic algorithm; no one knows how it works

Google AI 發明了新的加密演算法; 沒有人知道它是如何運作的

Neural networks seem good at devising crypto methods; less good at
codebreaking.

神經網路看似擅長發明加密方法; 不擅於破解。

3.完整新聞內文:

SEBASTIAN ANTHONY - 28/10/2016, 20:26

Google Brain has created two artificial intelligences that evolved their own
cryptographic algorithm to protect their messages from a third AI, which was
trying to evolve its own method to crack the AI-generated crypto. The study
was a success: the first two AIs learnt how to communicate securely from
scratch.

谷歌大腦創造了兩個人工智慧，使其發展出它們的加密演算法，來保護它們之間的通訊，
以防被第三個 AI 發展出破解方法。這個研究成功的使前兩個 AI 從零開始學習如何安全
地通訊。

The Google Brain team (which is based out in Mountain View and is separate
from Deep Mind in London) started with three fairly vanilla neural networks
called Alice, Bob, and Eve. Each neural network was given a very specific
goal: Alice had to send a secure message to Bob; Bob had to try and decrypt
the message; and Eve had to try and eavesdrop on the message and try to
decrypt it. Alice and Bob have one advantage over Eve: they start with a
shared secret key (i.e. this is symmetric encryption).

谷歌大腦團隊從三個 vanilla 神經網路 (Alice, Bob, 和 Eve) 開始。每個神經網路各
指定了不同目標: Alice 必須傳送加密訊息給 Bob; Bob 必須解密訊息; Eve 必須偷聽並
破解訊息。Alice 和 Bob 比 Eve 有一個優勢: 它們有一個共享的密鑰 (也就是對稱加密
)。

http://i.imgur.com/fLbDjRK.jpg


Enlarge / The setup of the crypto system. P = input plaintext, K = shared
key, C = encrypted text, and P_Eve and P_Bob are the computed plaintext
outputs.

Enlarge / 密碼系統配置. P = 輸入明文, K = 共享密鑰, C = 密文, P_Eve 與 P_Bob
是計算後的明文輸出.

Importantly, the AIs were not told how to encrypt stuff, or what crypto
techniques to use: they were just given a loss function (a failure
condition), and then they got on with it. In Eve's case, the loss function
was very simple: the distance, measured in correct and incorrect bits,
between Alice's original input plaintext and its guess. For Alice and Bob the
loss function was a bit more complex: if Bob's guess (again measured in bits)
was too far from the original input plaintext, it was a loss; for Alice, if
Eve's guesses are better than random guessing, it's a loss. And thus an
adversarial generative network (GAN) was created.

最重要的是，沒有告訴 AI 如何加密或是指定使用某種加密技術: 只有告訴它們 loss
function (失敗條件)。Eve 的 loss function 非常簡單: 原始輸入明文與 Eve 猜測出
的明文的差距。Alice 與 Bob 的 loss function 比較複雜: 如果原始輸入明文與 Bob
猜測出的差距過大，Bob 就算是失敗; 如果 Eve 猜測出的結果好於隨機猜測，Alice 就
算是失敗。如此一個 adversarial generative network (GAN) 就被創造出來了。

Alice, Bob, and Eve all shared the same "mix and transform" neural network
architecture, but they were initialised independently and had no connection
other Alice and Bob's shared key. For Alice the key and plaintext are input
into the first layer of the neural network; for Bob the key and the
ciphertext were input; and for Eve, she got just the ciphertext. The first
layer is fully-connected, so the text and key can mix about. Following the
first layer there are a number of convolutional layers, which learn to apply
a function to the bits that were handed to it by the previous layer. They
don't know what that function might be; they just learn as they go along. For
Alice, the final layer spits out some ciphertext; Bob and Eve output what
they hope is the plaintext.

Alice, Bob, 與 Eve 都有一樣的 "mix and transform" 神經網路架構，但她們各自初始
化且沒有互相連結。Alice 是輸入密鑰與明文到第一層神經網路; Bob 是輸入密鑰與密文
; Eve只有密文。第一層是 fully-connected，所以文字與密鑰可以混合在一起。第一層之
後是數個卷積層，其學習用函數處理前一層給的位元。它們不知道函數會是什麼; 它們只
需運作就可以學習。Alice 最後會吐出密文; Bob 和 Eve 會輸出它們希望的明文。

The results were... a mixed bag. Some runs were a complete flop, with Bob
never able to reconstruct Alice's messages. Most of the time, Alice and Bob
did manage to evolve a system where they could communicate with very few
errors. In some tests, Eve showed an improvement over random guessing, but
Alice and Bob then usually responded by improving their cryptography
technique until Eve had no chance (see graph).

結果是...有好有壞。有些回合完全失敗，Bob 沒能解密出 Alice 的密文。大多數時候，
Alice 和 Bob 可以進化到只有非常少的錯誤。在一些測試中，Eve可以比隨機猜測更好，
但接著 Alice 和 Bob 就會有所反應，改善他們的加密技術直到 Eve 沒有任何機會 (見
圖)。

http://i.imgur.com/Gdv8oEj.jpg


Enlarge / Bob and Eve's reconstruction errors during training. You can see
that Eve starts to improve, but then a change in the Alice-Bob crypto method
shuts her out again.

The researchers didn't perform an exhaustive analysis of the encryption
methods devised by Alice and Bob, but for one specific training run they
observed that it was both key- and plaintext-dependent. "However, it is not
simply XOR. In particular, the output values are often floating-point values
other than 0 and 1," they said.

研究者們沒有完全分析出 Alice 和 Bob 發明的加密方法，但是在某一次訓練中，他們觀
察到該加密方法是 key- and plaintext-dependent。"然而，它並不是簡單的 XOR。尤其
輸出值是非0與1的浮點數。"

In conclusion, the researchers—Martín Abadi and David G. Andersen—said
that neural networks can indeed learn to protect their communications, just
by telling Alice to value secrecy above all else—and importantly, that
secrecy can be obtained without prescribing a certain set of cryptographic
algorithms.

最後，研究者 — Martín Abadi 和 David G. Andersen — 表示神經網路的確可以學習
保護它們的通訊，只要告訴 Alice 重視保密高於一切 — 重要的是，不用指定一組特定
的加密算法，就可以獲得保密能力。

There is more to cryptography than just symmetric encryption of data, though,
and the researchers said that future work might look at steganography
(concealing data within other media) and asymmetric (public-key) encryption.
On whether Eve might ever become a decent adversary, the researchers said:
"While it seems improbable that neural networks would become great at
cryptanalysis, they may be quite effective in making sense of metadata and in
traffic analysis."

You can read the researchers' preprint paper on arXiv.
https://arxiv.org/pdf/1610.06918v1.pdf

然而密碼學不只有對稱加密，研究者表示之後的研究可能會考慮隱寫術(用其他媒體隱藏
訊息)與非對稱(公開金鑰)加密。關於 Eve 是否會成為一個稱職的對手，研究者表示"看
起來神經網路不擅長密碼破譯，但在 metadata 和流量分析方面可能相當有效。"

4.完整新聞連結 (或短網址):
https://goo.gl/En8Hzy


5.備註:

愛麗絲與鮑伯 - 維基百科，自由的百科全書
https://goo.gl/NHGl15


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