How Physna uses deep learning to search with 3D objects
Cincinnati, Ohio-based Physna uses deep learning to make three-dimensional shapes searchable.
Search engines were the key drivers of the web’s popularity, connecting websites and allowing users to find what they were looking for. Those search engines initially relied on text, and in more recent years have also allowed queries via 2D images - so-called reverse image searches.
Searching with objects
Despite the recent proliferation of 3D files - whether models for 3D printing or CAD software, or photogrammetric scans - it has not been possible to search via geometry. Which is where Physna comes in.
"Physna has enabled a quantum leap in technology by allowing software to truly understand physical 3D data. By merging the physical with the digital, we have unlocked massive and ever-growing opportunities in everything from geometric search to 3D machine learning and predictions," Paul Powers, CEO and founder of Physna.
Using deep-learning, Physna’s software turns 3D models into computer-understandable data, which then lets engineers and designers find similar models to the parts they need. Its customers include the likes of the Department of Defense, while the company also runs a powered by the same technology.
The future of search
Since its 2016 foundation, the company has raised across three funding rounds, with its latest alone raising $20mn. That recently announced round was led by Sequoia Capital, with the participation of Drive Capital.
"Paul and the Physna team have developed a breakthrough platform that enables intuitive search of 3D models for the first time," Shaun Maguire, partner at Sequoia. "With the amount of 3D data in the world about to explode, Physna will be the way this data is organized and accessed—ultimately, becoming the GitHub for 3D models."
"Having both Sequoia and Drive endorse this next-generation of search and machine learning helps Physna empower even more technical innovations for our customers and the market as a whole," added Powers.
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’.