When nas meets robustness: In search of robust architectures against adversarial attacks
Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep
neural networks. Since then, extensive efforts have been devoted to enhancing the …
neural networks. Since then, extensive efforts have been devoted to enhancing the …
Adversarial deepfakes: Evaluating vulnerability of deepfake detectors to adversarial examples
S Hussain, P Neekhara, M Jere… - Proceedings of the …, 2021 - openaccess.thecvf.com
Recent advances in video manipulation techniques have made the generation of fake
videos more accessible than ever before. Manipulated videos can fuel disinformation and …
videos more accessible than ever before. Manipulated videos can fuel disinformation and …
Feature distillation: Dnn-oriented jpeg compression against adversarial examples
Image compression-based approaches for defending against the adversarial-example
attacks, which threaten the safety use of deep neural networks (DNN), have been …
attacks, which threaten the safety use of deep neural networks (DNN), have been …
Motivating the rules of the game for adversarial example research
Advances in machine learning have led to broad deployment of systems with impressive
performance on important problems. Nonetheless, these systems can be induced to make …
performance on important problems. Nonetheless, these systems can be induced to make …
Macer: Attack-free and scalable robust training via maximizing certified radius
Adversarial training is one of the most popular ways to learn robust models but is usually
attack-dependent and time costly. In this paper, we propose the MACER algorithm, which …
attack-dependent and time costly. In this paper, we propose the MACER algorithm, which …
Towards efficient adversarial training on vision transformers
Abstract Vision Transformer (ViT), as a powerful alternative to Convolutional Neural Network
(CNN), has received much attention. Recent work showed that ViTs are also vulnerable to …
(CNN), has received much attention. Recent work showed that ViTs are also vulnerable to …
Meta gradient adversarial attack
In recent years, research on adversarial attacks has become a hot spot. Although current
literature on the transfer-based adversarial attack has achieved promising results for …
literature on the transfer-based adversarial attack has achieved promising results for …
Towards efficient and effective adversarial training
G Sriramanan, S Addepalli… - Advances in Neural …, 2021 - proceedings.neurips.cc
Abstract The vulnerability of Deep Neural Networks to adversarial attacks has spurred
immense interest towards improving their robustness. However, present state-of-the-art …
immense interest towards improving their robustness. However, present state-of-the-art …
A survey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies
Deep neural networks have achieved remarkable success in machine learning, computer
vision, and pattern recognition in the last few decades. Recent studies, however, show that …
vision, and pattern recognition in the last few decades. Recent studies, however, show that …
Machine learning security: Threats, countermeasures, and evaluations
Machine learning has been pervasively used in a wide range of applications due to its
technical breakthroughs in recent years. It has demonstrated significant success in dealing …
technical breakthroughs in recent years. It has demonstrated significant success in dealing …