Nvidia’s AI-powered Broadcast app for improved home audio
Many of us have become all too acutely aware of the lacking quality of microphones as the COVID-19 pandemic has made remote working the norm.
Not only that, but background noise can be a problem, whether from pets, children or anything else.
One possible solution to that comes from Nvidia and harnesses the power of AI. Nvidia says its new Broadcast app can upgrade bog-standard rooms and microphones by using AI to remove noise. In , Gerardo Delgado, Senior Product Manager, said: “The new NVIDIA Broadcast app [...] upgrades any room into a home broadcast studio by transforming standard webcams and microphones into smart devices through the power of AI.”
The Broadcast plugin is only available on Nvidia’s latest GeForce RTX, TITAN RTX or Quadro RTX GPUs, making use of their built-in Tensor Core AI processors.
The move comes after of British semiconductor giant Arm, which specialises in the more general purpose chips found in smartphones, heralding Nvidia’s potential arrival on the CPU as well as GPU stage.
Nvidia has long since pursued a strategy to move away from its heartland of manufacturing graphical processing units for gaming to also leveraging their highly parallel computing capabilities for machine learning and other AI endeavours.
In to Nvidia employees announcing the Arm purchase, Nvidia CEO Jensen Huang spoke of the potential of AI, saying: “We are joining arms with Arm to create the leading computing company for the age of AI. AI is the most powerful technology force of our time. Learning from data, AI supercomputers can write software no human can. Amazingly, AI software can perceive its environment, infer the best plan, and act intelligently. This new form of software will expand computing to every corner of the globe. Someday, trillions of computers running AI will create a new internet — the internet-of-things — thousands of times bigger than today’s internet-of-people.”
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’.