Generative adversarial networks or dueling Neural Networks can bestow AI systems with “imagination” and also poison AI defences

Most AI advances and applications are based on a type of algorithm known as machine learning that finds and reapplies patterns in data. Deep learning, a powerful subset of machine learning, uses neural networks to find and amplify even the smallest patterns.

 

Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, pitting one against the other (thus the “adversarial”). Currently, neural network can learn to recognize cats by analyzing several million cat photos, but humans must carefully identify the images and label them as cat photos. GANs can deliver unsupervised learning, systems that can learn without such heavy human involvement.

 

The members of Forbes Technology Council has  selected this as the next big thing in tech.  GaN is a way of pitting two neural networks against each other in order to train one of them to produce new things. For example, it may generate realistic-looking pictures. We’re not quite there yet but we will be soon. When that happens, it could conceivably become impossible to separate real information from false. For better or worse, that will be a very big thing,”  said Ben Lee, Rootstrap.  A new technique in artificial intelligence called GANs – which gives machines imagination – could soon be integrated into cloud services to make them cheaper and easier to use, MIT predict.

 

Since 2014, when GANs became popular through the work of Goodfellow (now a researcher at Google Brain), GANs have been used mainly to create realistic visuals with amazing results. In 2017, the graphics microprocessor company Nvidia trained a GAN, using photographs of celebrities, to produce its own ultra-realistic images of famous people who have never existed in the real world. GANs have been used to create videos that simulate the future few seconds of a scene, change day to night or summer to winter in landscape videos, fill in empty gaps in images or add years to faces, among many other spectacular demonstrations.

 

GANs  are responsible for the first piece of AI-generated artwork sold at Christie’s, as well as the category of fake digital images known as “deepfakes.”  GAN have raised concerns too,  “The ability to generate realistic artificial images, often called deepfakes when images are meant to look like recognizable people, has raised concern in recent years,” said Johnson.

 

Security companies have rushed to embrace AI models as a way to help anticipate and detect cyberattacks. However, sophisticated hackers could try to corrupt these defenses. “AI can help us parse signals from noise,” says Nate Fick, CEO of the security firm Endgame, but “in the hands of the wrong people,” it’s also AI that’s going to generate the most sophisticated attacks. Generative adversarial networks, or GANs, which pitch two neural networks against one another, can be used to try to guess what algorithms defenders are using in their AI models. Another risk is that hackers will target data sets used to train models and poison them—for instance, by switching labels on samples of malicious code to indicate that they are safe rather than suspect.

 

Now, images of the Earth are also subject to deep fake. 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 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. An adversary might fool computer-assisted imagery analysts into reporting that a bridge crosses an important river at a given point, while there is no bridge. Forces trained to go a certain route toward a bridge will, in fact, be surprised to find that the bridge does not exist.

 

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.

 

 

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.

 

 

China has been recently leading the efforts in using generative adversarial networks (GANs) to trick computers into seeing objects in landscapes or in satellite images that aren’t there. Todd Myers, automation lead for the CIO-Technology Directorate at the US National Geospatial-Intelligence Agency says “the Chinese have already designed; they’re already doing it right now, using GANs .. to manipulate scenes and pixels to create things for nefarious reasons.”

 

“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.

 

The military and intelligence community can defeat GAN, Myers claims, 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. “The biggest thing is the funding,” he said. U.S. officials confirmed that data integrity is a rising concern. Lt. Gen. Jack Shanahan, who runs the Pentagon’s new Joint Artificial Intelligence Center, said: “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 public open-source data and images, used by everybody, the question of how to protect it remains open.

 

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.”