Artificial Intelligence and People Profiling
The company behind the AI digital profiling system, VibraImage, claims its technology can identify how a person is feeling and their type of personality. The uses and applications of this type of AI technology include identifying ‘suspect’ individuals among crowds of people and grading the mental and emotional state of employees. Users of Vibralmage include police forces and airport security firms, and the technology has already been deployed at a FIFA World Cup and a G7 Summit.
In Japan, clients of such systems include the leading facial recognition provider, NEC, as well as Fujitsu and Toshiba. In South Korea, among other uses, it is being developed as a contactless lie detection system for use in police interrogations. In China, it has been officially certified for police use to identify suspicious individuals at airports and border crossings.
The technology works by using digital video surveillance and analysing the footage of involuntary micro-movements, or ‘vibrations’ of a person’s head, which are caused by muscles and the circulatory system.
It was developed by Russian biometrist Viktor Minkin through his ELSYS Corp back in 2001. He puts forward two theories supporting the idea that such movements are tied to emotional states, based firstly on the idea that the body’s system responsible for balance and spatial orientation is related to psychology and emotions. Secondly, by drawing a direct link between specific emotional-mental states and energy expended by muscles. Minkin claims this energy can be measured through tiny vibrations of the head.
This means involuntary movement of the face and head are, therefore, emotion, intention, and personality made visible. On top of spotting ‘suspects’, supporters believe this data can be used to determine personality and identify those more likely to commit a crime. However, these claims appear unprovable, and there are few scientific articles on VibraImage published in academic journals with rigorous peer reviews.
And it is not the only AI system out there to do this. Others have been trialled; for example, Avatar has been tested on the US-Mexico border and iBorderCtrl at the EU’s borders. Both are designed to detect deception among migrants.
The broader algorithmic emotion recognition industry was already worth US$12 billion in 2018, and it is expected to reach US$37.1 billion by 2026. As a result, there is growing concern around the need for rules on its ethics and more is required to decipher the technology’s use in forms of surveillance.
The European Commission’s announcement of draft AI regulations categorising the use of emotion recognition systems by law enforcement as ‘high risk’ and subject to higher levels of governance control is a good start in minimising any potential harms which could be caused by such systems.
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’.