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’.
Nvidia’s platform for AI startups passes 8,500 members
NVIDIA Inception, an acceleration platform for AI startups, has now surpassed 8,500 members. That’s about two-thirds of the total number of AI startups worldwide, as estimated by Pitchbook.
NVIDIA Inception is a programme built to accommodate every startup that is accelerating computing, at every stage in their journey. All programme benefits are free of charge and startups never have to give up equity to join.
Since Inception’s launch in 2016, it has grown more than tenfold. With total cumulative funding of over $60 billion and members in 90 countries, NVIDIA Inception is one of the largest AI startup ecosystems in the world. Growth has accelerated year over year, with membership increasing to 26% in 2020, and reaching 17% in the first half of 2021.
Data from across the world
Inception figures show the United States leads the world in terms of both the number of AI startups, representing nearly 27%, and the amount of secured funding, accounting for over $27 billion in cumulative funding. 42% of US-based startups were in California, with 29% in the San Francisco Bay Area.
Behind the US is China, in terms of both funding and company stage, with 12% of NVIDIA Inception members based there. India comes in third at 7%, with the UK right behind at 6%.
AI startups based in the US, China, India, and the UK account for just over half of all startups in NVIDIA Inception. Following in order after these are Germany, Russia, France, Sweden, Netherlands, Korea and Japan.
In terms of industries, healthcare, IT services, intelligent video analytics (IVA), media and entertainment (M&E) and robotics are the top five in NVIDIA Inception. AI startups in healthcare account for 16% of Inception members, followed by those in IT services at 15%.
More than 3,000 AI startups have joined Nvidia Inception since 2020. “Some countries are accelerating their ecosystem of AI startups by investing money and encouraging the local players to create more companies,” said Serge Lemonde, global head of Nvidia Inception, in an interview with VentureBeat.
“In our programme, what we are looking at is to help them all,” Lemonde said. “The lesson here is really having this window on the landscape and helping the startups all around the world — [this] is helping us understand the new trends. We can help more startups by developing our software and platforms for the upcoming trends.”