Apr 23, 2021

MIT develops AI to measure stress exertion on materials

AI
Engineering
machinelearning
GenerativeAdversarialNetwork
Sam Steers
2 min
Courtesy of Getty Images
The Massachusetts Institute of Technology (MIT) has created artificial Intelligence that calculates stress on materials using imagery...

The Massachusetts Institute of Technology announced yesterday it has developed an artificial intelligence (AI) tool with the ability to measure stress forces on materials.

Developed by MIT researchers, the tool is able to make estimations of the stresses exerted on materials in real-time. 

Briefly explaining how it works,McAfee Professor of Engineering and Director of the Laboratory, Markus Buehler, said: “From a picture, the computer is able to predict all those forces: the deformations, the stresses, and so forth.”

To turn the idea into reality, the researchers used a Generative Adversarial Network enhanced by using several thousands of images which showed a material’s microstructure after exertion. 

According to MIT, the network is able to solve the connection between the appearance of the material and the forces placed on it, using game theory. 

The AI is also able to replicate problems such as cracks developing which affect how the material reacts to stress. 

The GAN will run on consumer-grade computer processors once fully developed, making carrying out inspections easier and the AI more accessible in the field. 

Taking about the physics of force exertion, Buehler said: “Many generations of mathematicians and engineers have written down these equations and then figured out how to solve them on computers.

“But it’s still a tough problem. It’s very expensive - it can take days, weeks, or even months to run some simulations. So, we thought: “Let's teach an AI to do this problem for you,” he concluded. 

More information on the project can be found here.

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Jun 17, 2021

Facebook Develops AI to Crackdown on Deepfakes

Facebook
MSU
AI
Deepfakes
3 min
Social media giant, Facebook, has developed artificial intelligence that can supposedly identify and reverse-engineer deepfake images

In light of the large tidal wave of increasingly believable deepfake images and videos that have been hitting the feeds of every major social media and news outlet in recent years, global organisations have started to consider the risk factor behind them. While the majority of deepfakes are created purely for amusement, their increasing sophistication is leading to a very simple question: What happens when a deepfake is produced not for amusement, but for malicious intent on a grander scale? 

 

Yesterday, Facebook revealed that it was also concerned by that very question and that it had decided to take a stand against deepfakes. In partnership with Michigan State University, the social media giant presented “a research method of detecting and attributing deepfakes that relies on reverse engineering from a single AI-generated image to the generative model used to produce it.” 

 

The promise is that Facebook’s method will facilitate deepfake detection and tracing in real-world settings, where the deepfake image itself is often the only information detectors have to work with. 

Why Reverse Engineering? 

Right now, researchers identify deepfakes through two primary methods: detection, which distinguishes between real and deepfake images, and image attribution, which identifies whether the image was generated using one of the AI’s training models. But generative photo techniques have advanced in scale and sophistication over the past few years, and the old strategies are no longer sufficient. 

 

First, there are only so many images presented in AI training. If the deepfake was generated by an unknown, alternative model, even artificial intelligence won’t be able to spot it—at least, until now. Reverse engineering, common practice in machine learning (ML), can uncover unique patterns left by the generating model, regardless of whether it was included in the AI’s training set. This helps discover coordinated deepfake attacks or other instances in which multiple deepfakes come from the same source. 

 

How It Works 

Before we could use deep learning to generate images, criminals and other ill-intentioned actors had a limited amount of options. Cameras only had so many tools at their disposal, and most researchers could easily identify certain makes and models. But deep learning has ushered in an age of endless options, and as a result, it’s grown increasingly difficult to identify deepfakes.

 

To counteract this, Facebook ran deepfakes through a fingerprint estimation network (FEN) to estimate some of their details. Fingerprints are essentially patterns left on an image due to manufacturing imperfections, and they help identify where the image came from. By evaluating the fingerprint magnitude, repetition frequency, and symmetrical frequency, Facebook then applied those constraints to predict the model’s hyperparameters. 

 

What are hyperparameters? If you imagine a generative model as a car, hyperparameters are similar to the engine components: certain properties that distinguish your fancy automobile from others on the market. ‘Our reverse engineering technique is somewhat like recognising [the engine] components of a car based on how it sounds’, Facebook explained, ‘even if this is a new car we’ve never heard of before’. 

 

What Did They Find? 

‘On standard benchmarks, we get state-of-the-art results’, said Facebook research lead Tal Hassner. Facebook added that the fingerprint estimation network (FEN) method can be used for not only model parsing, but detection and image attribution. While this research is the first of its kind, making it difficult to assess the results, the future looks promising. 


Facebook’s AI will introduce model parsing for real-world applications, increasing our understanding of deepfake detection. As cybersecurity attacks proliferate, and generative AI falls into the hands of those who would do us harm, this method could help the ‘good guys’ stay one step ahead. As Hassner explained: ‘This is a cat-and-mouse game, and it continues to be a cat-and-mouse game’.

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