Apr 15, 2021

Cloudflare partners with Nvidia on web edge AI project

AI
machinelearning
edge
Cloud
William Smith
2 min
The collaboration between Nvidia and Cloudflare is intended to deploy machine learning models quickly with low latency via the edge

Web security firm Cloudflare has announced a partnership with AI leader Nvidia to bring AI to the edge.

Artificial intelligence has become ever more integrated into web pages, for instance translating text into a user’s language. The issue is that machine learning models can be computationally heavy, and thus slow down user interaction with a website. A remedy is pushing pre-trained machine learning models to edge networks where they are needed.

That’s exactly what the collaboration between Nvidia and Cloudflare is intended, with a platform using application frameworks, including Nvivia’s Jarvis for natural language processing, to deploy machine learning models quickly with low latency via Cloudflare’s data centres.
 

“Putting machine learning at the edge”
 

“As companies are increasingly data-driven, the demand for AI technology grows,” said Kevin Deierling, senior vice president of networking at NVIDIA. “NVIDIA offers developers AI frameworks to support applications ranging from robotics and healthcare to smart cities and now cybersecurity with the recently launched Morpheus.”

As an example of the AI possibilities it makes available, the company built a website to differentiate between foods (in particular identifying Pastel de Nata) via a TensorFlow Model.

Cloudflare’s Chief Technology Officer John Graham-Cumming said: “Previously machine learning models were deployed on expensive centralized servers or using cloud services that limited them to ‘regions’ around the world. Cloudflare and NVIDIA are putting machine learning at the edge, within milliseconds of the global online population, enabling high performance, low latency AI to be deployed by anyone.”
 

Digital twins another AI frontier


The Cloudflare partnership is one of a number of collaborations Nvidia has recently been involved in. Elsewhere, it said it was working with infrastructure engineering software firm Bentley Systems to bring AI to the making of digital twins, using Nvidia’s Omniverse platform to enhance real-time visualisation and simulation as well as to provide advanced analytics.

Bentley Systems CEO Greg Bentley said: “GPU-computing is transforming the world of engineering and construction and promises to unleash the potential of AI for simulation and advanced analytics in infrastructure digital twins.”

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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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