May 17, 2021

LTTS creates AI-based solution for smart parking

AI
Technology
Cloud
smartparking
2 min
L&T Technology Services (LTTS) has developed a parking guidance system solution in collaboration with Intel

L&T Technology Services Limited, a global engineering services company, announced today that it has worked with Intel Corporation to develop an outdoor smart parking solution. The solution is powered by the Intel distribution of the OpenVINO Toolkit to run AI inferencing models on Intel Xeon scalable processors and Intel Movidius VPUs.

The solution has edge AI capabilities and aims to change the outdoor smart parking experience in public areas. It has four key components: an operator portal that hosts user information; a mobile application for end-user interface; a digital signage module to ensure safe and secured access; and a digital camera - all connected by the AWS cloud platform. This technology should help end users easily locate available parking spaces in outdoor parking lots.  

Personalised experience 

The app offers a personalised experience, enabling reservation of parking spots and real-time occupancy tracking, and providing parking insights via an AI-enabled surveillance for operators through augmented video analytics. 

Amit Chadha, CEO & Managing Director, L&T Technology Services, said: “Technology and engineering services have the potential to not only enable business benefits but also empower organisations to proactively enhance environmental, social and governance related pursuits. With the density of urban environments resulting in wastage of fuel that runs into the thousands of liters every day, such an intelligent solution can help overcome a myriad of issues faced by industry. Using technologies from a global technology leader like Intel, we look forward to further opportunities to introduce disruptive innovations for the larger benefit of humankind.”  

The smart parking solution can be installed in parking areas in airports, stadiums, shopping malls, and office campuses. 

Jonathan Wood, Senior Director, Next Generation and Standards, Intel Corporation commented: “This latest edge AI-based parking innovation is a natural extension of LTTS’s product portfolio leveraging Intel technologies. Their use of Intel solutions, in the area of smart venues and buildings, further serves as a commitment to consistently introduce disruptive technologies to help Enterprise realise efficiencies from digital transformation.” 

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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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