Researchers develop AI to fool facial recognition tech

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A team of engineering researchers from the University of Toronto have 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 with Eureka Alert.


AI researchers design 'privacy filter' for your photos: New algorithm protects users' privacy by dynamically disrupting facial recognition tools designed to identify faces in photos

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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," says Aarabi. "This is one way in which beneficial anti-facial-recognition systems can combat that ability." Their solution leverages 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 working to identify faces, and the second working to disrupt the facial recognition task of the first.


AI researchers design 'privacy filter' for your photos

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IMAGE: Researchers in U of T Engineering have designed a'privacy filter' that disrupts facial recognition algorithms. The system relies on two AI-created algorithms: one performing continuous face detection, and another... view more 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," says Aarabi.


Artificial Intelligence Researchers Design 'Privacy Filter' For Photos

#artificialintelligence

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."


U of T Engineering AI researchers design 'privacy filter' for your photos that disables facial recognition systems - U of T Engineering News

#artificialintelligence

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 (ECE) and graduate student Avishek Bose (ECE MASc candidate) have created an algorithm to dynamically disrupt facial recognition systems. "Personal privacy is a real issue as facial recognition becomes better and better," says Aarabi. "This is one way in which beneficial anti-facial-recognition systems can combat that ability."