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.”
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. The two are constantly battling and learning from each other, setting up an ongoing AI arms race.
The result is an Instagram-like filter that can be applied to photos to protect privacy. Their algorithm alters very specific pixels in the image, making changes that are almost imperceptible to the human eye.
“The disruptive AI can ‘attack’ what the neural net for the face detection is looking for,” says Bose. “If the detection AI is looking for the corner of the eyes, for example, it adjusts the corner of the eyes so they’re less noticeable. It creates very subtle disturbances in the photo, but to the detector they’re significant enough to fool the system.”
Aarabi and Bose tested their system on the 300-W face dataset, an industry standard pool of more than 600 faces that includes a wide range of ethnicities, lighting conditions and environments. They showed that their system could reduce the proportion of faces that were originally detectable from nearly 100 per cent down to 0.5 per cent.
“The key here was to train the two neural networks against each other — with one creating an increasingly robust facial detection system, and the other creating an ever stronger tool to disable facial detection,” says Bose, the lead author on the project. The team’s study will be published and presented at the 2018 IEEE International Workshop on Multimedia Signal Processing later this summer.
In addition to disabling facial recognition, the new technology also disrupts image-based search, feature identification, emotion and ethnicity estimation, and all other face-based attributes that could be extracted automatically.
Next, the team hopes to make the privacy filter publicly available, either via an app or a website.
“Ten years ago these algorithms would have to be human defined, but now neural nets learn by themselves — you don’t need to supply them anything except training data,” says Aarabi. “In the end they can do some really amazing things. It’s a fascinating time in the field, there’s enormous potential.”
The Latest on: Deep learning
via Google News
The Latest on: Deep learning
- A Massive Opportunity Exists To Build “Picks And Shovels” For Machine Learningon March 22, 2020 at 8:28 pm
The reward for building the next generation of developer tools for machine learning will be many billions of dollars of enterprise value.
- Analysis on North America's Deep Learning Chipset Industry, 2019-2025: Anticipating a CAGR of 35.4%on March 21, 2020 at 8:33 am
The "North America Deep Learning Chipset Market, by Type, by Technology, by End User, by Country, Industry Analysis and Forecast, 2019 - 2025" report has been added to ResearchAndMarkets.com's ...
- Deep Learning Predicts Stroke-Lesion Changes at 1 Weekon March 20, 2020 at 6:47 am
A deep learning algorithm is comparable or even superior to common clinical measures for predicting infarct size and location up to a week following acute ischemic stroke, new research suggests.
- A multistep deep learning framework for the automated detection and segmentation of astrocytes in fluorescent images of brain tissueon March 20, 2020 at 3:12 am
While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in ...
- Hybrid AI systems are quietly solving the problems of deep learningon March 19, 2020 at 3:54 am
Deep learning, the main innovation that has renewed interest in artificial intelligence in the past years, has helped solve many critical problems in computer vision, natural language processing, and ...
- Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectureson March 19, 2020 at 3:08 am
Computer vision has greatly advanced recently. Since AlexNet was first introduced, many modified deep learning architectures have been developed and they are still evolving. However, there are few ...
- Deep learning changes scientific research, finds antibiotic for multi-resistant bacteriaon March 18, 2020 at 12:55 pm
Scientists at MIT and Harvard’s Broad Institute and MIT’s CSAIL built a deep learning network that can acquire a broad representation of molecular structure and thereby discover novel antibiotics.
- Deep Learning Market Size Record Booming 51.1% CAGR by 2020 to 2026on March 18, 2020 at 4:22 am
The global Deep Learning market is analyzed to grow at a CAGR of around 51.1% during the forecast period and projected to reach the market value over US$ 56,427.2 Mn in 2027. The study provides an ...
- Deep-learning A.I. is helping archaeologists translate ancient tabletson March 17, 2020 at 2:20 am
Deep-learning artificial intelligence is helping grapple with plenty of problems in the modern world. But it also has its part to play in helping solve some ancient problems as well — such as ...
via Bing News