Apr 19, 2021

Autonomous drones go interplanetary with NASA’s Ingenuity

AI
autonomousvehicles
drones
NASA
William Smith
2 min
The extreme distance of Mars from the Earth means real-time communication is impossible, therefore autonomous drones such as Ingenuity are the only option
The extreme distance of Mars from the Earth means real-time communication is impossible, therefore autonomous drones such as Ingenuity are the only opti...

We’ve recently witnessed a spate of companies developing autonomous drone technologies for use in everything from shopping delivery to human transportation.

One such example is Volocopter, which last month raised $240mn for its pursuit of urban autonomous taxis. It may be on Mars, however, that the technology realises its full potential, as the launch of NASA’s Ingenuity drone, part of its Perseverance mission, attests.

The extreme distance of Mars from the Earth means anything approaching real-time communication is impossible (as anyone who has watched The Martian will appreciate). Therefore, the only solution available is letting the drone react to conditions on Mars autonomously.

“We take risks other missions cannot”

MiMi Aung, Ingenuity Mars Helicopter Project Manager at NASA's Jet Propulsion Laboratory, said: “Ingenuity is a technology experiment. As such, our plan is to push the envelope and learn by doing. We take risks that other missions cannot, weighing each step carefully.”

By the time you’re reading this, Ingenuity’s maiden voyage should already have happened, although data on whether the experiment was a success will only reach earth around 10:30 GMT.

Autonomous test flight

Don’t expect anything too Marsshaking for Ingenuity’s first flight, with the simple tests involving take off, a 20-30 second hovering period, and landing. Assuming this first step is a success, Ingenuity is set for a series of test flights over the next 30 Martian days, each of which are 37 minutes longer than here on Earth.

“Our team considers Monday’s attempted first flight like a rocket launch: We’re doing everything we can to make it a success, but we also know that we may have to scrub and try again,” continued Aung. “In engineering, there is always uncertainty, but this is what makes working on advanced technology so exciting and rewarding. We have to continually innovate and develop solutions to new challenges. And we get to try things others have only dreamed of.”

(Image: NASA)

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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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