Adversarial Examples Detection and Analysis with Layer-wise Autoencoders
This addresses the security issue of adversarial attacks in machine learning models, providing a detection mechanism that is incremental in improving existing methods.
The paper tackles the problem of detecting adversarial examples by using layer-wise autoencoders to analyze data representations in hidden layers, achieving state-of-the-art performance in both supervised and unsupervised settings.
We present a mechanism for detecting adversarial examples based on data representations taken from the hidden layers of the target network. For this purpose, we train individual autoencoders at intermediate layers of the target network. This allows us to describe the manifold of true data and, in consequence, decide whether a given example has the same characteristics as true data. It also gives us insight into the behavior of adversarial examples and their flow through the layers of a deep neural network. Experimental results show that our method outperforms the state of the art in supervised and unsupervised settings.