Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks

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Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks

目录概主要内容 Mustafa A., Khan S., Hayat M., Goecke R., Shen J., Shao L., Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, arXiv preprint arXiv:1904.00887, 2019. 代码概类似的, 是large margin的思想, 亮点是多层? 主要内容以下, \(f^l\)表示第\(l\)层的输入,…

Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

目录概主要内容算法一些有趣的指标鲁棒性定义合格的抗干扰机制 Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami, Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks. 概本文提出一种distillation model, 能够免疫大部分的adversarial attacks,…

课程一(Neural Networks and Deep Learning)，第三周（Shallow neural networks）—— 3.Programming Assignment : Planar data classification with a hidden layer

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《C-RNN-GAN: Continuous recurrent neural networks with adversarial training》论文笔记

出处:arXiv: Artificial Intelligence, 2016(一年了还没中吗?) Motivation 使用GAN+RNN来处理continuous sequential data,并训练生成古典音乐 Introduction In this work, we investigate the feasibility of using adversarial training for a sequential model with continuous data, and eva…

Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! arXiv:2001.01587v1 [cs.NE] 1 Jan 2020 Abstract 脉冲神经网络(SNN)被广泛应用于神经形态设备中,以模拟大脑功能.在这种背景下,SNN的安全性变得重要但缺乏深入的研究,这与深度学习的热潮不同.为此,我们针对SNN的对抗攻击,确认了与ANN攻击不同的几个挑战:i)当前的对抗攻击是基于SNN中以时空模式呈现的梯度信息,这在传统的学习算法中很难获得:ii)在梯度累积过程中,输入的连续梯度与二值脉…

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Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! arXiv:2003.10399v2 [cs.CV] 23 Jul 2020 ECCV 2020 1 https://github.com/ssharmin/spikingNN-adversarial-attack Abstract 在最近对可信任的神经网络的探索中,我们提出了一个潜在的候选,即脉冲神经网络(SNN)之于对抗攻击的内在鲁棒性.在这项工作中,我们证明对CIFAR数据集上的深度VGG和ResNet结构,在基于梯度的攻击下,…

Adversarial Detection methods

目录 Kernel Density (KD) Local Intrinsic Dimensionality (LID) Gaussian Discriminant Analysis (GDA) Gaussian Mixture Model (GMM) SelectiveNet Combined Abstention Robustness Learning (CARL) Adversarial Training with a Rejection Option Energy-based Out-of…

Speech Super Resolution Generative Adversarial Network

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CSAGAN：LinesToFacePhoto: Face Photo Generation from Lines with Conditional Self-Attention Generative Adversarial Network - 1 - 论文学习

ABSTRACT 在本文中,我们探讨了从线条生成逼真的人脸图像的任务.先前的基于条件生成对抗网络(cGANs)的方法已经证明,当条件图像和输出图像共享对齐良好的结构时,它们能够生成视觉上可信的图像.然而,这些模型无法合成具有完整定义结构的人脸图像,例如眼睛.鼻子.嘴巴等,特别是当条件线图缺少一个或多个部分时.为了解决这一问题,我们提出了一个条件自注意生成对抗网络(CSAGAN).我们在cGANs中引入了条件自注意机制来捕获面部不同区域之间的长范围依赖关系.我们还建立了一个多尺度判别器.大规模判别…