On the robustness of vision transformers to adversarial examples
Recent advances in attention-based networks have shown that Vision Transformers can
achieve state-of-the-art or near state-of-the-art results on many image classification tasks …
achieve state-of-the-art or near state-of-the-art results on many image classification tasks …
Back in black: A comparative evaluation of recent state-of-the-art black-box attacks
The field of adversarial machine learning has experienced a near exponential growth in the
amount of papers being produced since 2018. This massive information output has yet to be …
amount of papers being produced since 2018. This massive information output has yet to be …
How to Defend and Secure Deep Learning Models Against Adversarial Attacks in Computer Vision: A Systematic Review
L Dhamija, U Bansal - New Generation Computing, 2024 - Springer
Deep learning plays a significant role in develo** a robust and constructive framework for
tackling complex learning tasks. Consequently, it is widely utilized in many security-critical …
tackling complex learning tasks. Consequently, it is widely utilized in many security-critical …
Adversarial adaptive neighborhood with feature importance-aware convex interpolation
Adversarial Examples threaten to fool deep learning models to output erroneous predictions
with high confidence. Optimization-based methods for constructing such samples have been …
with high confidence. Optimization-based methods for constructing such samples have been …
Beware the black-box: On the robustness of recent defenses to adversarial examples
Many defenses have recently been proposed at venues like NIPS, ICML, ICLR and CVPR.
These defenses are mainly focused on mitigating white-box attacks. They do not properly …
These defenses are mainly focused on mitigating white-box attacks. They do not properly …
Practical black-box adversarial attack on open-set recognition: Towards robust autonomous driving
As an important method of image classification, Open-Set Recognition (OSR) has been
gradually deployed in autonomous driving systems (ADSs) for detecting the surrounding …
gradually deployed in autonomous driving systems (ADSs) for detecting the surrounding …
[BOOK][B] Generative Models as a Robust Alternative for Image Classification: Progress and Challenge
A Ju - 2021 - search.proquest.com
The tremendous success of neural networks is clouded by the existence of adversarial
examples: maliciously engineered inputs can cause neural networks to perform abnormally …
examples: maliciously engineered inputs can cause neural networks to perform abnormally …
Rοbustness οf neural netwοrk image classifiers tο meaningful adversarial examples
L Anquetil - 2023 - theses.hal.science
Machine learning is revolutionizing the world in many ways, enabling the creation of artificial
systems capable of performing complex tasks. In medicine, machine learning systems are …
systems capable of performing complex tasks. In medicine, machine learning systems are …
[BOOK][B] Designing Deep Networks for Adversarial Robustness and Security
KR Mahmood - 2022 - search.proquest.com
The advent of adversarial machine learning fundamentally challenges the widespread
adoption of Convolutional Neural Networks (CNNs), Vision Transformers and other deep …
adoption of Convolutional Neural Networks (CNNs), Vision Transformers and other deep …