Aug 19, 2020

UK considers allowing hands-free autonomous driving

William Smith
2 min
The UK government has announced it is consulting on allowing the use of Automated Lane Keeping systems in the UK
The UK government has announced it is consulting on allowing the use of Automated Lane Keeping systems in the UK...

The UK government has announced it is consulting on allowing the use of Automated Lane Keeping systems in the UK.

Such technologies take over a vehicle’s steering, allowing it to automatically remain in lane while driving - though the driver must remain prepared to reassume control.

One of the areas the consultation is looking at is whether any incidents involving such systems are the responsibility of the vehicle manufacturer or automation technology provider.

The end-goal for manufacturers is achieving a fully autonomous vehicle ranked level 5 on the Society of Automotive Engineers (SAE) Levels of Driving Automation Standard, meaning they are able to operate in all conditions without human interaction. To date, no solution has achieved that, with commercial offerings typically falling into levels 2 and 3. Level 2 denotes a vehicle with automated steering and acceleration features, such as stay-in-lane and self-parking (the kind being considered in the UK), while level 3 is indicative of a vehicle capable of detecting the environment surrounding it to, for instance, overtake other vehicles.

Such stay in lane systems could come to UK roads by Spring 2021, depending on the results of the consultation, at speeds of up to 70mph.

The UK lags behind other countries in deploying the technology, particularly when it comes to China and the US, two nexuses of automated vehicle development. In the latter, one of the leading contenders is Google’s self driving car project, Waymo, which at the start of the year achieved 20 million autonomous miles of testing on public roads. The company has recently partnered with Volvo to integrate its Waymo Driver product, which can be built into third-party vehicles to afford them autonomous capabilities.

In China, DiDi leads the way. China’s Uber equivalent has received permission to test its autonomous fleet in the Jiading district of Shanghai, with vehicles on level 4 of the SAE scale, meaning they are capable of operating in all conditions without human intervention. Although disrupted by the COVID-19 pandemic, plans included a further roll out to cities such as Beijing and Shenzhen, and even California in 2021.

Share article

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’.

Share article