Deep neural networks (DNN) have been shown to be useful in a wide range of applications.
However, they are also known to be vulnerable to adversarial samples. By transforming a …

Defense models against adversarial attacks have grown significantly, but the lack of
practical evaluation methods has hindered progress. Evaluation can be defined as looking …

Object detection is an important vision task and has emerged as an indispensable
component in many vision system, rendering its robustness as an increasingly important …

Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to
adversarial attacks, making them prohibitive in some real-world applications. By converting …

Adversarial training is a powerful type of defense against adversarial examples. Previous
empirical results suggest that adversarial training requires wider networks for better …

Over the last few years, the phenomenon of adversarial examples---maliciously constructed
inputs that fool trained machine learning models---has captured the attention of the research …

Advances in the development of adversarial attacks have been fundamental to the progress
of adversarial defense research. Efficient and effective attacks are crucial for reliable …

While the transferability property of adversarial examples allows the adversary to perform
black-box attacks ie, the attacker has no knowledge about the target model), the transfer …

Perturbative availability poisoning (PAP) adds small changes to images to prevent their use
for model training. Current research adopts the belief that practical and effective approaches …

The adversarial training procedure proposed by Madry et al.(2018) is one of the most
effective methods to defend against adversarial examples in deep neural networks (DNNs) …