Tesco Drone Delivery Service: The Trial
One of the largest supermarket giants, have released a statement notifying the public of their new AI induced delivery service.
The announcement of a Tesco drone delivery service, makes it one of the organisations to join the list of companies aiming to provide automotive deliveries in the near future. The trial period will launch in October 2020, based from County Galway in Ireland. They will endeavor to deliver ‘small baskets’ of consumer goods to the local area, within the first 30 minutes of placing the order. The setup process mirrors that of Amazon’s Prime Air programme, currently underway and well into their journey of understanding and developing the data-engineered technology needed to make this fictional thought into a reality. Trails taking place will aid Tesco in the understanding of which processes are in need of improvement, and give an overall result of how successful and in demand the need for this service will be.
Back in 2016, Amazon made their first drone delivery within the United Kingdom - from their fulfilment base in Cambridge, to a nearby local resident. The order was delivered within 13 minutes of the original order being placed. From 2016 to 2020, the leap has been made of unmanned aerial vehicles (UAVs) delivering essential hospital supplies - mainland to the Isle of Wight. Although this is a significant step forward within the world of AI technology, there has inevitably been opposing opinions on the subject of drone deliveries. The matter of privacy has been voiced by some residence, not overjoyed at the thought of drones operating above gardens and within private property. They believe it to be intrusive. Although this is a ligitermate query, the largest force of opposition is propelled by the thought of noise pollution. Thought to be too noisy whilst operating, the aircrafts are facing backlash from, not only local residents, but nationwide. Within the previous year, an Institution of Mechanical Engineers report illustrated that only a quarter of UK adults, enthused their support for the idea of drone deliveries. The results showed the main causes for these issues - noise and safety.
Facebook Develops AI to Crackdown on Deepfakes
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’.