Sep 10, 2020

Amazon Prime Air

AI
Delivery
technology transformation
automation
Amber Naylor
2 min
Innovation in a world for the ultimate consumer.
Innovation in a world for the ultimate consumer...

 

In a world full of voice activation and smart technologies, the addition of unmanned aircraft systems should not seem surprising. Sounding more like an innovation from futuristic science fiction, Amazon is pushing the boundaries of automation technologies and transporting the future impossibilities into present potentiality. 

Breaking down barriers in AI development, Amazon Prime Air promises the control systems of autonomous drones, to deliver packages within 30 minutes of placing your order. Consumerism gone mad or previously believed unattainable advances in the world of delivery services? The aim is to enhance a system that is already provided to millions of their customers, using drone operations as the leading modernization for the organisation. Turning vision into reality; a modern day miracle in the world of technological advances. 

Amazon wants to create a world in which seeing a delivery drone in the sky will become as commonplace as noticing mailing trucks on the road. What does this require from Amazon - machine learning and rapidly experimenting with integrating on Prime Air modifications, utilising their next generation research labs. 

Can we expect tomorrow’s technology today? The public have yet to receive a determined date for the release of this state-of-the-art approach to purchase consumer goods, but have been continually updated with results from tested trails within the United Kingdom. Further tests are said to be taking place, expanding the area of delivery services. Wanting to continually jolt the perimeter of how technology - services and digital infrastructure - are able to make improvements within the customer experience sphere and assist in making the economics of the countries located within their operating field, more competitive on a global scale. 

Putting the system into service will become one of this decade’s most significant transformations within the AI and Technology worlds. Developing how the world as we know it operates; social normalities will be unfalteringly decided by the metamorphosis’ we witness in the very near future. The ultimate paradise for all consumers. Watch this space. 

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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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