English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Pattern lock is a general technique used to realize identity authenticationand access authorization on mobile terminal devices such as Android platformdevices, but it is vulnerable to the attack proposed by recent researches thatexploit information leaked by users while drawing patterns. However, theexisting attacks on pattern lock are environmentally sensitive, and relyheavily on manual work, which constrains the practicability of these attackapproaches. To attain a more practical attack, this paper designs thePatternMonitor, a whole pipeline with a much higher level of automation systemagainsts pattern lock, which extracts the guessed candidate patterns from avideo containing pattern drawing: instead of manually cutting the target videoand setting thresholds, it first employs recognition models to locate thetarget phone and keypoints of pattern drawing hand, which enables the gesturecan be recognized even when the fingertips are shaded. Then, we extract theframes from the video where the drawing starts and ends. These pre-processedframes are inputs of target tracking model to generate trajectories, andfurther transformed into possible candidate patterns by performing our designedalgorithm. To the best of our knowledge, our work is the first attack system togenerate candidate patterns by only relying on hand movement instead ofaccurate fingertips capture. The experimental results demonstrates that ourwork is as accurate as previous work, which gives more than 90\% success ratewithin 20 attempts.

PatternMonitor: a whole pipeline with a much higher level of automation for guessing Android lock pattern based on videos