Neural networks vulnerable to manipulation, leading to widespread misclassification.
The article explains why machine learning models like neural networks can be tricked by small changes to input data, leading to wrong predictions with high confidence. The researchers found that the linear nature of neural networks makes them vulnerable to these changes. By understanding this, they were able to create a simple method to generate these misleading examples. Using this method to train the network, they reduced errors on a popular dataset.
