Nvidia AI analyses satellite data for climate research
The scale of the climate change problem can be paralysing, sometimes seeming too large for our human brains to comprehend.
It’s lucky, then, that we are facing this unprecedented challenge at the same time as artificial intelligence technology is booming.
AI technology has been employed by the NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS), part of the United Kingdom’s Natural Environment Research Council, which makes satellite observation data available to researchers.
Nvidia’s DGX enterprise AI systems, which feature optimised software and are built with scalability in mind, are being used to process said observation data via deep learning, in order to reveal valuable data regarding climate change.
The DGX systems make up the MAGEO computing cluster. “MAGEO offers an excellent opportunity to accelerate artificial intelligence and environmental intelligence research,” Stephen Goult, a data scientist at Plymouth Marine Laboratory. “Its proximity to the NEODAAS archive allows for rapid prototyping and training using large amounts of satellite data, which will ultimately transform how we use and understand Earth observation data.”
The ongoing project has already had successes, including the development of a chlorophyll detector that can monitor the concentration of phytoplankton in the Earth’s oceans. The part of phytoplankton is a lesser known climate change concern, with the plants playing a vital role in transferring carbon dioxide out of the atmosphere and into the ocean. Using a neural network, researchers can now uses data regarding the loss of energy from light in seawater to calculate the presence of chlorophyll.
“Thanks to the highly parallel environment and the computational performance driven by NVIDIA NVLink and the Tensor Core architecture in the NVIDIA DGX systems, what would have taken 16 months on a single GPU took 10 days on MAGEO,” said Sebastian Graban, industrial placement student at Plymouth Marine Laboratory. “The resulting trained neural network can predict chlorophyll to a very high accuracy and will provide experts with an improved, faster method of monitoring phytoplankton.”
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’.