Apr 28, 2021

UK plans to allow self-driving autonomous vehicles on roads

Automation
self-driving
autonomousvehicles
UK
William Smith
2 min
The government said that self-driving vehicles could eventually boost road safety by reducing human error and bring an end to urban congestion
The government said that self-driving vehicles could eventually boost road safety by reducing human error and bring an end to urban congestion...

The UK’s Department For Transport has announced that self-driving vehicles could be present on British roads by the end of the year.

There has been some controversy over the terminology, with only Automated Lane Keeping Systems (ALKS) to be initially allowed at speeds of up to 37mph on motorways (essentially in slow traffic situations). Such systems only reach level 2 of the Society of Automotive Engineers’ (SAE) Levels of Driving Automation Standard, with the targeted level 5 representing complete autonomy at all times. The fear is that referring to such systems as self-driving could lead to complacency on the part of drivers.

The safety of autonomous driving

The perils of autonomous driving have recently made headlines for Tesla, after a fatal crash in Texas that resulted in the deaths of two men. The company’s Autopilot feature was initially blamed, something which the company denies was enabled.

Transport Minister Rachel Maclean said: “This is a major step for the safe use of self-driving vehicles in the UK, making future journeys greener, easier and more reliable while also helping the nation to build back better. “But we must ensure that this exciting new tech is deployed safely, which is why we are consulting on what the rules to enable this should look like. In doing so, we can improve transport for all, securing the UK’s place as a global science superpower.”

The government said that self-driving vehicles could actually boost road safety by reducing human error, which it said was a factor in more than 85% of accidents. It also anticipated the technology could bring an end to urban congestion, with smart coordination between traffic lights and autonomous vehicles, as well as lead to improved public transport.

“Higher levels of automation in the future”

Society of Motor Manufacturers and Traders Chief Executive Mike Hawes said: “Technologies such as Automated Lane Keeping Systems will pave the way for higher levels of automation in future – and these advances will unleash Britain’s potential to be a world leader in the development and use of these technologies, creating essential jobs while ensuring our roads remain among the safest on the planet.”

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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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