美專家警告：中國用新技術製假地圖 擾亂各國 自由 20190404

【Comment】

This is what Beijing really is.  It will do whatever it can, ignoring the consequences and moral principles.

https://www.moonlol.com/deepfakes-ai-fakeapp-6208.html

美專家警告：中國用新技術製假地圖 擾亂各國    自由 20190404

近期「AI換臉」（Deepfakes）技術相當盛行，能製出以假亂真的動態人臉畫面。不過，美國國家地理情報局專家31日指出，這項技術也可以應用在軍事上，而中國在這方面技術領先於美國，甚至能在衛星圖上製出不存在的影像或建築物，來擾亂其他國家的偵察。

據《防務一號》（Defenseone）報導，美國國家地理情報局局長辦公室的自動化項目主管兼首席資訊總監邁爾斯（Todd Myers）聲稱，中國是首個利用「生成對抗網路」（Generative Adversarial Networks，GAN）技術來對衛星圖進行加工，試圖欺騙、擾亂外界偵察的國家，「他們已經成功欺騙了電腦，讓電腦辨識出不存在的地形和目標。」據悉，在GAN技術中，最廣為人知的應用就是「AI換臉」。

邁爾斯解釋，中國利用GAN技術來操縱圖像和像素，並進一步製出不存在的東西。例如，當中國在某處河流製造出「假橋」後，接著對手就會針對這座橋，在被引誘的情況下制定戰術。他說：「中國早就有計畫要來實現『邪惡目的』。」

邁爾斯指出，中國學者從2017年起，就開始利用這項技術來識別衛星照片中的道路和橋梁等目標物，「先不說國防和情報方面會遇到的問題，想像一下，若是Google地圖真的被混入假資訊，那麼5年後，當特斯拉自動駕駛系統被廣泛使用後，又會如何？」

The Newest AI-Enabled Weapon: ‘Deep-Faking’ Photos of the Earth    Defense One 20190331

Step 1: Use AI to make undetectable changes to outdoor photos.

Step 2: release them into the open-source world and enjoy the chaos.

Worries about deep fakes — machine-manipulated videos of celebrities and world leaders purportedly saying or doing things that they really didn’t — are quaint compared to a new threat: doctored images of the Earth itself.

China is the acknowledged leader in using an emerging technique called generative adversarial networks to trick computers into seeing objects in landscapes or in satellite images that aren’t there, says Todd Myers, automation lead for the CIO-Technology Directorate at the National Geospatial-Intelligence Agency.

“The Chinese are well ahead of us.  This is not classified info,” Myers said Thursday at the second annual Genius Machines summit, hosted by Defense One and Nextgov.  “The Chinese have already designed; they’re already doing it right now, using GANs—which are generative adversarial networks—to manipulate scenes and pixels to create things for nefarious reasons.”

For example, Myers said, an adversary might fool your computer-assisted imagery analysts into reporting that a bridge crosses an important river at a given point. 

“So from a tactical perspective or mission planning, you train your forces to go a certain route, toward a bridge, but it’s not there.  Then there’s a big surprise waiting for you,” he said.

First described in 2014, GANs represent a big evolution in the way neural networks learn to see and recognize objects and even detect truth from fiction.

Say you ask your conventional neural network to figure out which objects are what in satellite photos.  The network will break the image into multiple pieces, or pixel clusters, calculate how those broken pieces relate to one another, and then make a determination about what the final product is, or, whether the photos are real or doctored.  It’s all based on the experience of looking at lots of satellite photos.

GANs reverse that process by pitting two networks against one another — hence the word “adversarial.”  A conventional network might say, “The presence of x, y, and z in these pixel clusters means this is a picture of a cat.” But a GAN network might say, “This is a picture of a cat, so x, y, and z must be present. What are x, y, and z and how do they relate?” The adversarial network learns how to construct, or generate, x, y, and z in a way that convinces the first neural network, or the discriminator, that something is there when, perhaps, it is not.

A lot of scholars have found GANs useful for spotting objects and sorting valid images from fake ones.  In 2017, Chinese scholars used GANs to identify roads, bridges, and other features in satellite photos.

The concern, as AI technologists told Quartz last year, is that the same technique that can discern real bridges from fake ones can also help create fake bridges that AI can’t tell from the real thing.

Myers worries that as the world comes to rely more and more on open-source images to understand the physical terrain, just a handful of expertly manipulated data sets entered into the open-source image supply line could create havoc.  “Forget about the [Department of Defense] and the [intelligence community].  Imagine Google Maps being infiltrated with that, purposefully?  And imagine five years from now when the Tesla [self-driving] semis are out there routing stuff?” he said.

When it comes to deep fake videos of people, biometric indicators like pulse and speech can defeat the fake effect.  But faked landscape isn’t vulnerable to the same techniques.

Even if you can defeat GANs, a lot of image-recognition systems can be fooled by adding small visual changes to the physical objects in the environment themselves, such as stickers added to stop signs that are barely noticeable to human drivers but that can throw off machine vision systems, as DARPA program manager Hava Siegelmann has demonstrated.

Myers says the military and intelligence community can defeat GAN, but it’s time-consuming and costly, requiring multiple, duplicate collections of satellite images and other pieces of corroborating evidence.  “For every collect, you have to have a duplicate collect of what occurred from different sources,” he said.  “Otherwise, you’re trusting the one source.”

The challenge is both a technical and a financial one.

“The biggest thing is the funding required to make sure you can do what I just talked about,” he said.

On Thursday, U.S. officials confirmed that data integrity is a rising concern.  “It’s something we care about in terms of protecting our data because if you can get to the data you can do the poisoning, the corruption, the deceiving and the denials and all of those other things,” said Lt. Gen. Jack Shanahan, who runs the Pentagon’s new Joint Artificial Intelligence Center.  “We have a strong program protection plan to protect the data.  If you get to the data, you can get to the model.”

But when it comes to protecting open-source data and images, used by everybody from news organizations to citizens to human rights groups to hedge funds to make decisions about what is real and what isn’t, the question of how to protect it is frighteningly open.  The gap between the “truth” that the government can access and the “truth” that the public can access may soon become unbridgeable, which would further erode the public credibility of the national security community and the functioning of democratic institutions.

Andrew Hallman, who heads the CIA’s Digital Directorate, framed the question in terms of epic conflict.  “We are in an existential battle for truth in the digital domain,” Hallman said.  “That’s, again, where the help of the private sector is important and these data providers.  Because that’s frankly the digital conflict we’re in, in that battle space…This is one of my highest priorities.”

When asked if he felt the CIA had a firm grasp of the challenge of fake information in the open-source domain, Hallman said, “I think we are starting to.  We are just starting to understand the magnitude of the problem.”