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Bio-Inspired Adversarial Attack Against Deep Neural Networks
Hence in a successful physical attack against DNN, targeted motion against the system should also be considered.
Adversarial Machine Learning for Cybersecurity and Computer Vision: Current Developments and Challenges
Research in adversarial machine learning addresses a significant threat to the wide application of machine learning techniques -- they are vulnerable to carefully crafted attacks from malicious adversaries.
Understanding Adversarial Examples Through Deep Neural Network's Response Surface and Uncertainty Regions
Our study reveals the structural problem of DNN classification boundary that leads to the adversarial examples.
Breaking Transferability of Adversarial Samples with Randomness
We demonstrate that introducing randomness to the DNN models is sufficient to defeat adversarial attacks, given that the adversary does not have an unlimited attack budget.
Adversarial Clustering: A Grid Based Clustering Algorithm Against Active Adversaries
Most of the previous work focused on adversarial classification techniques, which assumed the existence of a reasonably large amount of carefully labeled data instances.
