Adversarial attacksText classificationThe loss-based implementationThe gradient-based implementationAdversarial examples are generated by adding infinitesimal perturbations to legitimate inputs so that incorrect predictions can be induced into deep learning models. They have received increasing attention recently ...
Decoupling Direction and Norm for Efficient Gradient-Based L_{2} Adversarial Attacks and Defenses 说在前面 CVPR 2019 原文链接:openaccess.thecvf.com/c 论文源码:github.com/jeromerony/f 模型: github.com/MadryLab 本文作于2022年3月24日 1.提出的问题...
The remainder of this article is shown as follows: in Section II, the knowledge about graph neural networks is described along with the current work related to adversarial attacks. A detailed description of the proposed method is given in Section III. The attack performance of the proposed method...
Adversarial attacks canbe divided into white-box and black-box attacks. White-boxattacks assume that all information about the target modelis transparent. Conversely, black-box attacks (Wang and He2021; Brendel, Rauber, and Bethge 2018; Ren et al. 2024)are more challenging due to the l......
Generation of synthetic full-scale burst test data for corroded pipelines using the tabular generative adversarial network. Engineering Applications of Artificial Intelligence, 2022, 115: 105308. DOI:10.1016/j.engappai.2022.105308 443. Chen, Y., Xu, Y., Jamhiri, B. et al. Predicting uniaxial ...
28 Oct 2021·Lifan Yuan,Yichi Zhang,Yangyi Chen,Wei Wei· Despite recent success on various tasks, deep learning techniques still perform poorly on adversarial examples with small perturbations. While optimization-based methods for adversarial attacks are well-explored in the field of computer vision...
Gradient-Based Adversarial Attacks Against Malware Detection by Instruction ReplacementDeep learning plays a vital role in malware detection. The Malconv is a well-known deep learning-based open source malware detection framework and is trained on raw bytes for malware binary detection. Researchers ...
Deep learning models suffer from a phenomenon called adversarial attacks: we can apply minor changes to the model input to fool a classifier for a particular example. The literature mostly considers adversarial attacks on models with images and other structured inputs. However, the adversarial ...
Deep neural networks (DNNs) are vulnerable to adversarial attacks which can fool the classifiers by adding small perturbations to the original example. The added perturbations in most existing attacks are mainly determined by the gradient of the loss function with respect to the current example. In...
Improved Gradient based Adversarial Attacks for Quantized NetworksKartik GuptaThalaiyasingam Ajanthan