Presenting adversarial.js Tool: An Interactive, In-Browser Demonstration Of Adversarial Attacks On Neural Networks

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Presenting adversarial.js Tool: An Interactive, In-Browser Demonstration Of Adversarial Attacks On Neural Networks

Kenny Song, a graduate student at the University of Tokyo, developed adversarial.js, an interactive tool that shows how adversarial attacks function using Tensorflow.js.

There has been increasing attention to the threat that adversarial attacks cause to cybersecurity systems. Some adversarial attack examples include glasses that fool facial recognition systems and the stickers pasted on stop signs causing computer vision systems to mistake them for speed limits.

AI researchers and cybersecurity professionals are now making efforts to educate people about adversarial attacks and create robust machine learning systems. Recently, adversarial.js was released on GitHub to raise awareness about machine learning security through the project.

Article: https://www.marktechpost.com/2020/12/02/presenting-adversarial-js-tool-an-interactive-in-browser-demonstration-of-adversarial-attacks-on-neural-networks/

Github: https://github.com/kennysong/adversarial.js

Demo: https://kennysong.github.io/adversarial.js/

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https://preview.redd.it/w3uynxoiys261.png?width=696&format=png&auto=webp&s=f056c32c5375e74eb6cbf8c4005a571c4756ad5f

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