Baidu and Geely partner on “intelligent” electric vehicles
Chinese search engine and technology giant Baidu is entering the eclectic vehicle market in partnership with compatriot automotive manufacturer Geely.
Baidu said it would provide “intelligent driving capabilities” powering vehicles designed and manufactured by Geely, parent company of a number of well known brands including Volvo and Lotus.
In , Robin Li, co-founder and CEO of Baidu, said: "At Baidu, we have long believed in the future of intelligent driving and have over the past decade invested heavily in AI to build a portfolio of world-class self-driving services. China has become the world's largest market for EVs, and we are seeing EV consumers demanding next generation vehicles to be more intelligent."
"As a top Chinese automaker with global reach, Geely has the unique experience and resources to design, produce and market energy-efficient, reliable and safe automobiles in large scale. We believe that by combining Baidu's expertise in smart transportation, connected vehicles and autonomous driving with Geely's expertise as a leading automobile and EV manufacturer, the new partnership will pave the way for future passenger vehicles."
This is not the first time Baidu has entered the autonomous driving space, with previous efforts based around its Apollo self-driving platform. At , the company demonstrated what it referred to as “fully automated driving” on a Weltmeister electric vehicle platform. The company also said, after six million kilometres of on-road testing with zero accidents, the system was capable of driving without a safety driver at the helm.
AI is one of Baidu’s main focuses, with one of its products being its AI technology platform. “Artificial intelligence is the core technology of the fourth-generation industrial revolution,” said Baidu CTO Haifeng Wang. “Baidu Brain can enable all industries to apply AI technology more efficiently, while accelerating the process of industrial intelligence.”
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’.