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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks笔记
Code https github com mzweilin EvadeML Zoo Feature squeezing reducing the color bit depth of each pixel and spatial smoo
Feature
Squeezing
Detecting
Adversarial
Examples
【论文学习】Robust Tracking against Adversarial Attacks论文学习
一 知识点补充 OTB中的评价指标 xff08 1 xff09 one pass evaluation xff08 OPE xff09 这是目标追踪领域常用的评估方式 xff0c 只给第一帧ground truth没有随机性的算法只跑一遍就
Robust
tracking
Against
Adversarial
Attacks
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey 论文阅读笔记
本文是论文的阅读笔记 Paper A Threat of Adversarial Attacks on Deep Learning in Computer Vision A Survey Author Naveed Akhtar cor n
Threat
Adversarial
Attacks
Deep
Learning
论文阅读笔记:GENERATING NATURAL ADVERSARIAL EXAMPLES
论文阅读笔记 xff1a GENERATING NATURAL ADVERSARIAL EXAMPLES 本文发表在ICLR2018上 问题 传统对抗样本是unnatural的 xff0c 在真实数据中几乎不存在 contribution
Generating
NATURAL
Adversarial
Examples
论文阅读笔记
[论文解读]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Threat of Adversarial Attacks on Deep Learning in Computer Vision A Survey 文章目录 Threat of Adversarial Attacks on Deep Le
Threat
Adversarial
Attacks
Deep
Learning
【论文笔记】(防御蒸馏)Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
有关蒸馏 Distillation 的论文 xff1a 2006 Model Compression 2014 Do Deep Nets Really Need to be Deep 论文笔记 2015 Distilling the Kno
Distillation
Defense
Adversarial
Perturbations
Against
Curriculum adversarial training
Weakness of adversarial training overfit to the attack in use and hence does not generalize to test data Curriculum adve
Curriculum
Adversarial
Training
AT-AWP:Adversarial weight perturbation helps robust generalization
本文提出一种简单而有效的对抗权重扰动 AWP 来明确规范化权重损失图的平滑度 xff0c 在对抗训练框架中形成双重扰动机制 输入扰动和权值扰动 大量实验表明 xff0c AWP确实使权重损失图更加平缓 xff0c 并且可以很容易地融入各种现
AWP
Adversarial
Weight
Perturbation
Helps
Clustering Effect of (Linearized) Adversarial Robust Models
2021 12 6 第三篇 xff08 NeurIPS 2021 xff09 半精读 原文链接 xff1a Clustering Effect of Linearized Adversarial Robust Models 代码链接 xff
Clustering
Effect
Linearized
Adversarial
Robust
【文献笔记】【精读】LEARNING TO PROTECT COMMUNICATIONS WITH ADVERSARIAL NEURAL CRYPTOGRAPHY
文章地址 xff1a Learning to Protect Communications with Adversarial Neural Cryptography 源码 xff08 第三方 xff09 xff1a pytorch http
Learning
protect
COMMUNICATIONS
with
Adversarial
[paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
本文提出了两种特征压缩方法 xff1a 减少每个像素的颜色位深度使用空间平滑来减少各个像素之间的差异 特征压缩通过将与原始空间中许多不同特征向量相对应的样本合并为单个样本 xff0c 从而减少了对手可用的搜索空间 通过将DNN模型对原始输入
Paper
Feature
Squeezing
Detecting
Adversarial
[转载][paper]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
文章目录 摘要 深度学习是当前人工智能崛起的核心 在计算机视觉领域 xff0c 它已经成为从自动驾驶汽车到监控和安全等各种应用的主力 虽然深度神经网络在解决复杂问题方面取得了惊人的成功 通常超出了人类的能力 xff0c 但最近的研究表明 x
Paper
Threat
Adversarial
Attacks
Deep
【论文阅读】Feature Denoising for Improving Adversarial Robustness
阅读由来SCRDet 43 43 参考文献 20 https blog csdn net dujuancao11 article details 121590324 Feature Denoising for Improving Adver
Feature
Denoising
for
Improving
Adversarial
Adversarial Attacks on deep learning阅读笔记
Adversarial Attacks on deep learning阅读笔记 简单说说Adversarial attackAdversarial Attacks on Deep Learning Based Radio Signal C
Adversarial
Attacks
Deep
Learning
阅读笔记
Threat of Adversarial Attacks on Deep Learning in Computer Vision A Survey
Attacks for classification Box constrained L BFGS Fast Gradient Sign Method FGSM Basic amp Least Likely Class Iterative
Threat
Adversarial
Attacks
Deep
Learning
面向自然语言处理的对抗攻防与鲁棒性分析综述 Survey of Adversarial Attack, Defense and Robustness Analysis for Natural Lang
6 面向自然语言处理的对抗攻防与鲁棒性分析综述 Survey of Adversarial Attack Defense and Robustness Analysis for Natural Language Processing 摘要
Survey
Adversarial
Attack
Defense
and
论文笔记——EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES(解释和利用对抗样本)
本文参考了三篇笔记 xff0c 帮助很大 xff1a Explaining and Harnessing Adversarial Examples 阅读笔记 论文笔记 Explaining amp Harnessing Adversaria
EXPLAINING
and
HARNESSING
Adversarial
Examples
[论文] Feature Squeezing:Detecting Adversarial Examples in Deep Neural Networks
思路 xff1a 对抗样本经过feature squeeze处理后大部分增加的干扰会被消除或者减小 xff0c 致使feature squeeze前后的分类结果向量 xff08 distributed vector xff09 L1距离很大
Feature
Squeezing
Detecting
Adversarial
Examples
Adversarial Robustness - Theory and Practice
文章目录 第一章 Introduction to adversarial robustness第二章 linear models第三章 Adversarial examples solving the inner maximization1
Adversarial
Robustness
Theory
and
Practice
Adversarial Weight Perturbation Helps Robust Generalization(AWP adversarial train )
目录 主要创新点实验探究Weight loss landscape与robust generalization gap之间的关系Weight loss landscape的绘制在learning processing of vanilla
Adversarial
Weight
Perturbation
Helps
Robust
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