Machine learning/artificial intelligence is an emerging field, and we wanted to apply this profound technology to an everyday use case - the smartphone. Privacy and security are especially important in the 21st century, and we wanted to apply machine learning fundamentals to learn a user's pattern unlocking tendencies and be able to detect intruders who may know the phone's unlock pattern.

What it does

The lockscreen is equipped with a neural network which can differentiate between the phone's owner and an intruder trying to unlock it. The initial set-up involves the unlock pattern being repeatedly drawn by both the owner and an outside individual so the neural network is able to detect subtle differences between users based on a variety of criteria. The AI is further trained and improved each time it is unlocked, either by the owner or a detected intruder.

How we built it

Neural network architecture was constructed, measuring various criteria and evaluating various mathematical operations based on these findings to build a profile of the phone's owner.

Challenges we ran into

Unable to get neural network's propagation algorithm to converge at a low enough error, overriding the home button on Android to get an actual lock screen.

Accomplishments that we're proud of

Building an efficient neural network that is up to 90% successful in our performed test cases.

What we learned

The intricacies of artificial intelligence; small operational changes can have large-scale impacts on the success rate.

What's next for LearningLock

Integrating additional functions to improve user experience (ex. widgets showing content relevant to the user).

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