Adversarial Machine Learning

Machine learning techniques were originally designed for stationary and benign environments in which the training and test data are assumed to be generated from the same statistical distribution. However, when those models are implemented in the real world, the presence of intelligent and adaptive adversaries may violate that statistical assumption to some degree, depending on the adversary. At the same time, adversarial samples can help identify weaknesses in an ML model, which, in turn, can be used to gain valuable insights on how to enhance the model. Links: WikipediaAdversarial Sample GenerationAdversarial Examples Fool both Computer Vision and Time-Limited Humans

This entry was posted in Machine Learning. Bookmark the permalink.

Leave a Reply

Please log in using one of these methods to post your comment:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s