Facebook's Moments app uses facial recognition technology to group photos based on the friends who are in them. Amid privacy concerns in Europe and Canada, the versions launched in those regions excluded the facial recognition feature. Facebook's Moments app uses facial recognition technology to group photos based on the friends who are in them. Amid privacy concerns in Europe and Canada, the versions launched in those regions excluded the facial recognition feature. When someone tags you in a photo on Facebook, it's often a nice reminder of a shared memory.
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.
Throughout the world, cities are getting smarter as connected devices become increasingly integral to a wide range of processes. Traffic management systems leverage real-time analyzation of video data to help keep people moving. Surveillance cameras help keep them protected and drive efficiencies of emergency first responders. IoT-enabled sensors are even capable of monitoring the structural health of buildings, critical infrastructure, and environmental conditions. These connected devices are designed to make our lives easier, safer, and more convenient, but they bring with them inevitable concerns about issues like privacy.
Each time you upload a photo or video to a social media platform, its facial recognition systems learn a little more about you. These algorithms ingest data about who you are, your location and people you know -- and they're constantly improving. As concerns over privacy and data security on social networks grow, U of T Engineering researchers led by Professor Parham Aarabi and graduate student Avishek Bose have created an algorithm to dynamically disrupt facial recognition systems. "Personal privacy is a real issue as facial recognition becomes better and better," said Aarabi. "This is one way in which beneficial anti-facial-recognition systems can combat that ability."
Recent weeks have brought controversy over electronic billboards in restaurants and shopping precincts that utilize advanced facial recognition techniques to not only provide personalized advertisements but also measure and record the consumer and their response, ostensibly to enable retailers to provide more targeted marketing and services. In Oslo, the restaurant Peppe's Pizza had its usage of such billboards exposed due to a crashed digital advertisement that revealed the coding behind its facial recognition system. The billboard includes a camera and facial recognition software that can register gender, whether the watcher is young or an adult, facial expression, whether they wear glasses. In response, Dublin-based designer Youssef Sarhan did a little digging in his home time of Dublin and also discovered similar billboards in operation. "Your attention (and the meta-data associated with it) is being relayed to advertisers without your permission or awareness, and there is no way to opt–out.