定义1(Feature Matching Distance) 两组图像之间的特征匹配距离定义为D(μ, ν),即两组经验分布μ和ν之间的OT距离。 特征散射:基于特征匹配,特征散射可以定义为: 这可以直观地解释为最大化原始和扰动经验分布之间的特征匹配距离,相对于受域约束 Sµ 的输入 定义2(Feature Scattering)给定一组干净的数据,可以将...
HSJA:根据边界label变化情况估计梯度(文中称为蒙特卡罗采样,其实可以从label变化方向上来直白地理解)。 Customized Adversarial Boundary (CAB):使用当前噪声选择敏感区域(以一定比例r去选择部分噪声,多种选择后使用选出来的噪声和最大部分的像素),以及采样分布定制更新,是本文作者之前的工作。 SurFree:在当前对抗样本点...
防御:最近的一种针对转移攻击的防御方法(Tramer等人,2017)基于对数据集的鲁棒训练,该数据集由一组替代模型的对抗性示例进行增强,已被证明在2017年Kaggle对抗性攻击竞赛中基本上成功地抵御了所有攻击(https://www.kaggle.com/c/nips-2017-defense-against-adversarial-attack) 可以轻松避免许多攻击的事实使得评估模型是否...
complexity of adversarial examples; 2) the ATGAN incorporates adversarial training into the standard GAN training procedure to remove obfuscated gradients which can lead to a false sense in defending against adversarial perturbations and are commonly observed in existing GANs-based adversarial defense ...
Adversarial perturbation 用于生成噪音,来欺骗神经网络分类器 Lu尝试生成对抗样本来干扰stop,欺骗识别器 Xie提出了稠密对抗生成DAG算法,迭代欺骗分类器 3. Method 原理图 image-20191124214707928.png 原理 给定输入图片、预训练好的RPN 设计目标函数:Label loss + Shape loss ...
However, the RUL methods based on deep learning demand sufficient and varied samples of critical components from run to failure. To compensate for the lack of samples, generative adversarial networks (GAN) and conditional generative adversarial networks (CGAN) are proposed [20–22]. Although GAN an...
Adversarial Attack on Skeleton-Based Human Action Recognition 基于骨架的人体动作识别的对抗攻击ST-GCN的对抗攻击 原文:https://arxiv.org/abs/1909.06500 背景 图卷积网络适合做基于骨架的人体动作识别 ST-GCN 但是鲁棒性没做过,因为骨骼关节的时空性质是稀疏和离散的,并且有物理约束 ...
In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly ...
Adversarial samplesDifferential evolutionBlack-box testingDeep Neural NetworkDeep Neural Networks (DNNs) are being used in various daily tasks such as object detection, speech processing, and machine translation. However, it is known that DNNs suffer from robustness problems -- perturbed inputs called ...
Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection笔记 摘要 基于对正常模型和鲁棒模型的观察---对抗样本会过度激活和预测类无关的通道,鲁棒类会激活和预测类正相关的类---假设抑制负相关通道,增强正相关通道就可以进一步提高对抗训练的鲁棒性。给这个猜想起个名字,即Channel...