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 ...
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...
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...
The examples are indeed interesting and showcase the limitations of SOTA pre-trained vision models on some of these images which are more complex for these models to interpret. Some of the reasons of failure can be attributed to what deep learning models are trying to focus on when making pre...
[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。
Our analysis of medical adversarial examples provides new interpretations of the learned representations and additional explanations for the decisions made by deep learning models in the context of medical images. This is a useful starting point towards building explainable and robust deep learning systems...
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
Recently, deep neural networks have been used to automatically analyze ECG tracings and outperform physicians in detecting certain rhythm irregularities1. However, deep learning classifiers are susceptible to adversarial examples, which are created from raw data to fool the classifier such that it ...
内容提示: 1Adversarial Examples: Attacks and Defenses forDeep LearningXiaoyong Yuan, Pan He, Qile Zhu, Rajendra Rana Bhat, Xiaolin LiNational Science Foundation Center for Big Learning, University of FloridaAbstract—With rapid progress and great successes in a widespectrum of applications, deep ...
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...