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Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake. An adversarial attacker could target autonomous vehicles by using stickers or paint to create an adversarial stop sign that the vehicle would interpret as a ‘yield’ or other sign. A confused car on…
The post Adversarial Reprogramming: Exploring A New Paradigm of Neural Network Vulnerabilities appeared first on Analytics India Magazine.
The reliability of a machine learning model is assessed based on how erroneous it is. Lesser the number of errors, better the prediction. In theory, ML models should be able to predict, classify and recommend right every single time. However, when deployed in the real world, the model has a very good chance of running…
The post How Reliable Are Neural Networks Classifiers Against Unforeseen Adversarial Attacks appeared first on Analytics India Magazine.