比如图像识别、目标检测、文本分析、推荐系统等等. 在图像识别上面, 深度学习的识别率甚至超过了人眼[1]. 深度学习在短时间内频繁取得的成功致使很多人倾向于相信他们已经找到了“疑难杂症”的终极解决方案:从此以后, 想要解决一个问题, 只需要找到足够多的数据和算力就行了. 但是对抗样本(Adversarial examples)的发现...
Deep learning architectures are vulnerable to adversarial perturbations. They are added to the input and alter drastically the output of deep networks. These instances are called adversarial examples. They are observed in various learning tasks from supervised learning to unsupervised and reinforcement ...
索引词:深度神经网络(deep neural network),深度学习(deep learning),安全(security),对抗样本(adversarial examples)。 1. 介绍 在机器学习(Machine learning, ML)的各种领域中,深度学习(Deep learning, DL)都取得了重大进展,例如图像分类(image classification)、目标识别(object recognition) [1][2]、目标检测(obj...
Introducing adversarial examples in vision deep learning models Introduction We have seen the advent of state-of-the-art (SOTA) deep learning models for computer vision ever since we started getting bigger and better compute (GPUs and TPUs), more data (ImageNet etc.) and easy to use open-so...
[paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks,程序员大本营,技术文章内容聚合第一站。
Deep neural networks are currently the most widespread and successful technology in artificial intelligence. However, these systems exhibit bewildering new vulnerabilities: most notably a susceptibility to adversarial examples. Here, I review recent empi
5.对抗防御 通常包括对抗训练、基于随机的方案、去噪方法、可证明防御以及一些其他方法。 5.1对抗训练 对抗训练:通过与对抗样本一起训练,来尝试提高神经网络的鲁棒性。 通常情况下,可视为如下定义的最大最小游戏: 其中,代表对抗代价,θ代表网络权重,x‘代表对抗输入
1. What are adversarial examples? 😈 In the last 10 years, deep learning models have left the academic kindergarten, become big boys, and transformed many industries. This is especially true for computer vision models. WhenAlexNethit the charts in 2012, the deep learning era officially started...
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Neural Networks论文笔记 0. 概述 如今一些深度神经网络对于一些对抗性样本(Adversarial sample)是弱势的, 对抗性样本就是指我们对输入进行特定的改变, 通过原有的学习算法最终导致整个网络内部出现误差, 这属于攻击的一种, 然而, 现在的攻击都是要...
文章精读-《Understanding adversarial examples requires a theory of artefacts for deep learning》-20241230 Sonder 摘要: 观点: 深度神经网络目前是人工智能领域最为普及且成效显著的技术。然而,这些系统也展现出令人困惑的新漏洞,其中最显著的是对对抗样本的敏感性。本文回顾了近期关于对抗样本的实证研究,这些研究表...