This repository implements the targeted and untargeted versions of theStochastic Gradient Descent (SGD)algorithm (also known as Stochastic Projected Gradient Descent (sPGD) inMummadi et al.andDeng & Karam) for
Universal adversarial perturbations matlab: MATLAB code to generate universal perturbations, using Caffe or MatConvNet. python: Python code to generate universal perturbations using TensorFlow. precomputed: Precomputed universal perturbations, for pre-trained models on ImageNet. Reference [1] S. Moosavi-...
This repo is implementation of CVPR 2022 paper "Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations" Prerequisites We provide the dependency file of our experimental environment, you can install all dependencies by creating a new anaconda virtual environment and running pip ...
CVPR2017,原文链接:Universal adversarial perturbations 官方开源代码: github.com/LTS4/univers 请参阅此出版物的讨论、统计数据和作者简介:https://www.researchgate.net/publication/309460742 摘要 给定一个最先进的深度神经网络分类器,作者证明存在了一种通用的,图像不可知的和非常小的扰动向量,这会导致分类器以很高...
[论文笔记] Universal adversarial perturbations 说在前面 个人心得: 提出一种针对某个数据集的通用扰动的方法 用低维子空间做了一定的解释 CVPR 2017,原文链接:ieeexplore.ieee.org/doc 官方开源代码:github.com/LTS4/univers 本文作于2020年12月26日。 摘要 作者证明了对于SOTA的分类器,存在通用的、图像不可知...
Universal Adversarial Perturbations summary:证明了UAP的存在,提出了一个算法可以系统性地搜寻UAP。其证明UAP的泛化性很好,能够以一个相对高的置信度欺骗分类器。UAP并不仅仅存在于图像,而是广泛存在于各种神经网络中。 文章地址:openaccess.thecvf.com/c code:github.com/LTS4/univers 方法 UAP的计算过程 其实就是...
In this paper, for the first time, we show that a universal adversarial attack can also be achieved through spatial transformation (non-additive). More importantly, to unify both additive and non-additive perturbations, we propose a novel unified yet flexible framework for universal adversarial ...
Universal adversarial perturbations CVPR 2017 · Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard · Edit social preview Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector ...
In this paper, we use generative models to compute universal adversarial perturbations. The generator is not conditioned on the images and so creates a perturbation that can be applied to any image to create an adversarial example. We get pretty pictures like this: Clean Image + Perturbation ==...
The script "Universal_perturbations_multi.py" contains the implementation of the algorithm used for the generation of universal adversarial perturbations. The program takes as input the following parameters: model_path: path to the target model. We assume that the model is a frozen TF classifier (...