During just its third day in action, a facial recognition system used by Washington Dulles International Airport (IAD) caught its first imposter. While that's a clear win for proponents of the tech, it might also be major blow to the privacy of the average airline passenger. On Monday, 14 airports in the U.S. launched a pilot program to test the effectiveness of a biometric scanning system during the security and boarding processes. Passengers simply stand in front of a camera that takes their photo. The system then compares that photo to the one on the person's passport to confirm their identity.
Imagine a world in which you can scan your face to board a train, check into a hotel, order a meal at a café, or even track your food from farm to table. In China, all of this is already happening. Facial recognition became more pervasive this year after the Chinese government in December 2017 announced an ambitious plan to achieve greater face-reading accuracy by 2020. The country also plans to introduce a system that will identify any of its 1.3 billion citizens in just three seconds. Public and private enterprises have rushed to adopt the futuristic, artificial intelligence-powered technology, implementing facial-recognition systems in transportation networks, medical facilities, and law enforcement initiatives.
Data brokers already buy and sell detailed profiles that describe who you are. They track your public records and your online behavior to figure out your age, your gender, your relationship status, your exact location, how much money you make, which supermarket you shop at, and on and on and on. It's entirely reasonable to wonder how companies are collecting and using images of you, too. Facebook already uses facial recognition software to tag individual people in photos. Apple's new app, Clips, recognizes individuals in the videos you take.
A team of engineering researchers from the University of Toronto has created an algorithm to dynamically disrupt facial recognition systems. Led by professor Parham Aarabi and graduate student Avishek Bose, the team used a deep learning technique called "adversarial training", which pits two artificial intelligence algorithms against each other. Aarabi and Bose designed a set of two neural networks, the first one identifies faces and the other works on disrupting the facial recognition task of the first. The two constantly battle and learn from each other, setting up an ongoing AI arms race. "The disruptive AI can'attack' what the neural net for the face detection is looking for," Bose said in an interview.
The Department of Homeland Security (DHS) is trialing a new facial recognition technology at US borders aimed at keeping track of people as the enter and exit the country. Called the Vehicle Face System, the project is being spearheaded by Customs and Border Protection at the Anzalduas Border Crossing, located at the southern tip of Texas, in August, according to the Verge. Sophisticated cameras will take photos of people arriving and departing the US and match them with government documents like visas and passports. The cameras are expected to remain in operation at the crossing for a full year. A customs spokesperson told the Verge that the purpose of the project will be to'evaluate capturing facial biometrics of travelers entering and departing the US and compare those images to photos on file in government holdings'.