Dec 16, 2020

Tableau and the USAF: data as a strategic asset

William Smith
3 min
Tableau’s Michael Parker on the benefits its data analysis and visualisation platform brings to the United States Air Force
Tableau’s Michael Parker on the benefits its data analysis and visualisation platform brings to the United States Air Force...

Data analysis and visualisation company Tableau offers its customers the capacity to make better use of the data they have. Michael Parker is VP, Business Development at the company. “ Tableau's mission is simple,” he says. “We help people see and understand their data. We provide that through a single pane of glass view of their data in a secure environment, ensuring the right people have the right access to the right data at the right time.” The security of that data is another key concern, with the company striving to fulfill that aspect while ensuring it remains accessible. “The ability to use your data in this secure environment is critical. That’s accomplished by building a data trust initiative with customers, building accuracy, timeliness, quality measures into their data and ultimately creating a culture of literacy and knowledge around data. We don't expect everyone to be a data scientist, but we do see data literacy grow across organisations.”

Parker has 34 years of experience as a public servant, having recently transitioned from the DOD, and has accordingly seen a sea change in how data is used. “Data analytics and visualisation was classically used by data analysts for descriptive analytics, predictive analytics, but mostly for measurement tools and the creation of reports. That's changed significantly now with new, powerful weapons within analytic platforms like Tableau that allow real-time decision-making for both subject matter experts and senior leaders alike to draw insights from data.”

It’s that capability that is behind Tableau’s partnership with the United States Air Force, as Parker explains. “They’re looking at data as a strategic asset and as a common service component of digital transformation. We use the tools specifically around a couple of use cases that draw a great return on investment. One was civilian hiring. We needed to understand where the choke points are, where's the lag and the slack in the process. By pulling the data in from end-to-end in that whole civilian hiring process, we could look at it through an operational lens to really understand where we were experiencing challenges. Strategic decisions made along the way ultimately compressed the timeline by two thirds.”

With chief data offices now established in each of the services, Parker believes the full value of data is now being appreciated. In standing that up, it's been recognised that data is a strategic asset and a powerful tool for both the business and warfighting domains.” The partnership has also proved its worth in the response to the COVID-19 pandemic. “Having tools for personnel use, personnel accountability, tracking of individuals and even return to work processes was really important, and so the partnership was critical at that point.”

Parker emphasises that the partnership is built to last. “At Tableau, we plan to continue to build our partnership and understand the strategic and operational needs of the Defense Department and how the platform can help solve issues and provide capabilities in strengthening our partnership over time.” 

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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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