Oct 21, 2020

Intel and ESA launch experimental AI satellite PhiSat-1

PhiSat-1
satellite
AI
Intel
William Smith
2 min
American semiconductor giant Intel and the European Space Agency have launched what Intel calls the first “AI satellite” into space
American semiconductor giant Intel and the European Space Agency have launched what Intel calls the first “AI satellite” into space...

American semiconductor giant Intel and the European Space Agency have launched what Intel calls the first “AI satellite” into space.

The launch, which took place on 2 September, saw the experimental PhiSat-1 ejected from a rocket alongside 45 other small satellites.

PhiSat-1 is one of a pair of satellites intended to monitor polar ice and soil moisture, as well as test communication between satellites for a planned future network.

The satellite contains a thermal camera and, crucially, onboard AI processing capabilities thanks to an Intel Movidius Myriad 2 Vision Processing Unit. The processor will use AI techniques to improve imagery and reduce bandwidth by discarding “cloudy” images, as Gianluca Furano, data systems and onboard computing lead at the European Space Agency, explained in a press release.

“The capability that sensors have to produce data increases by a factor of 100 every generation, while our capabilities to download data are increasing, but only by a factor of three, four, five per generation. And artificial intelligence at the edge came to rescue us, the cavalry in the Western movie.”

Satellites in space are blasted with radiation outside of the protection of the Earth’s atmosphere, but Intel’s chip passed testing in its unaltered form.

Future uses for AI-enhanced satellites might include swapping out networks in a “satellite-as-a-service” model, as Jonathan Byrne, head of the Intel Movidius technology office explained: ““Rather than having dedicated hardware in a satellite that does one thing, it’s possible to switch networks in and out.”

Such possibilities will be tested with the PhiSat-2 satellite which is currently being developed to be capable of running different AI apps

Small satellites are very much in vogue thanks to the decrease in costs to orbit achieved by companies such as SpaceX. SpaceX itself is building a constellation known as Starlink in batches of 60, with the end-goal of 30,000 satellites providing satellite internet access globally.

(Image: Intel)

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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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