Protecting cognitive systems from gradient based attacks through the use of deceiving gradientsMechanisms are provided for providing a hardened neural network. The mechanisms configure the hardened neural network executing in the data processing system to introduce noise in internal feature representations of...
Decoupling Direction and Norm for Efficient Gradient-Based Adversarial Attacks and Defenses 说在前面 1.提出的问题 2.提出的方法 2.1 相关工作 2.2 算法介绍 3.实验结果 3.1 Untargeted Attack 3.2 Targeted Attack 3.3 Defense Evaluation 4.结论 Decoupling Direction and Norm for Efficient Gradient-Based L2 Ad...
Model-driven deep unrolling: Towards interpretable deep learning against noise attacks for intelligent fault diagnosis Intelligent fault diagnosis (IFD) has experienced tremendous progress owing to a great deal to deep learning (DL)-based methods over the decades. However, ... Z Zhao,T Li,B An,...
conda activate grad_attacks 2 TAG 复现结果和要点讨论 2.1 结果概览(GPT-2 自回归文本生成) 这里使用预训练的 GPT-2,任务为自回归文本生成,输入为一个 16 个 token 的序列: The Tower Building of the Little Rock Arsenal, also known as U.S. 也即预训练的 GPT-2 会学习自回归地生成以上序列,而...
In this work, we explore the implications of Gradient Inversion attacks in FL and propose a novel defence mechanism, called Pruned Frequency-based Gradient Defence (pFGD), to mitigate these risks. Our defence strategy combines frequency transformation using techniques such as Discrete Cosine Transform...
Furthermore, it is important to note that transfer-based attacks operated on a white-box surrogate model are evaluated on a black-box detector. Consequently, excessive iterations may lead to overfitting on the surrogate model, resulting in sub-optimal performance of adversarial examples on the ...
proposed an eXtreme Gradient Boosting (XGBoost) based DDoS attack detection mechanism in Chen et al. (2018a). By iteratively splitting nodes, XGBoost can construct a specified number of trees. Subsequently, it can identify whether the features belong to DDoS attacks. Four types of features ...
Significance: This library addresses the growing need for secure computing in the era of quantum computers, ensuring that AI applications remain secure against potential quantum-based attacks. If you enjoyed this post please support our work by encouraging your friends and colleagues to subscribe to ...
IV. ADVERSARIAL ATTACKS AGAINST SNNS 在这一部分中,我们首先简要介绍输入数据格式,然后详细说明我们的攻击方法的流程、方法和算法。 Input Data Format.SNN模型处理脉冲信号是很自然的。因此,考虑到包含脉冲事件的数据集,例如N-MNIST[33]和CIFAR10-DVS[34],这是首选。在这种情况下,输入最初是一个时空模式,每个元...
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...