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GitHub - mzweilin/EvadeML-Zoo: Benchmarking and …

EvadeML-Zoo. The goal of this project: Several datasets ready to use: MNIST, CIFAR-10, ImageNet-ILSVRC and more. Pre-trained state-of-the-art models to attack. [See details]. Existing attacking methods: FGSM, BIM, JSMA, Deepfool, Universal Perturbations, Carlini/Wagner-L2/Li/L0 and more. [See details]. Visualization of adversarial examples. …

Github.com

DA: 10 PA: 21 MOZ Rank: 31

GitHub - Erwin-rella/EvadeML-Zoo-detective: …

Github.com

DA: 10 PA: 34 MOZ Rank: 45

Can Machine Learning Ever Be Trustworthy? - Speaker Deck

David Evans University of Virginia [email protected] EvadeML.org Weilin Xu Yanjun Qi Funding: NSF, Intel, Baidu Xiao Zhang Center for Trustworthy Machine Learning David Evans University of Virginia [email protected] EvadeML.org

Speakerdeck.com

DA: 15 PA: 50 MOZ Rank: 67

Classifiers Under Attack - Speaker Deck

Talk at USENIX Enigma 2017 1 February 2017 Oakland, CA https://evadeML.org. Talk at USENIX Enigma 2017 1 February 2017 Oakland, CA https://evadeML.org. Upgrade to Pro — share decks privately, control downloads, hide ads and more … Speaker Deck. Speaker Deck. PRO. Sign in Sign up for free Classifiers Under Attack David Evans February 01, 2017 …

Speakerdeck.com

DA: 15 PA: 36 MOZ Rank: 54

Feature Squeezing: Detecting Adversarial Examples in ... - GitHub …

EvadeML.org Abstract—Although deep neural networks (DNNs) have achieved great success in many tasks, recent studies have shown they are vulnerable to adversarial examples. Such examples, typically generated by adding small but purposeful distortions, can frequently fool DNN models. Previous studies to defend against adversarial examples mostly focused on reﬁning …

Machine-learning-and-security.github.io

DA: 39 PA: 28 MOZ Rank: 71

A novel adversarial example detection method for ... - ScienceDirect

EvadeML attacks (Xu et al., 2016) uses genetic programming techniques to perform a directed search of the space of possible examples to find ones that evade the classifier while retaining the desired malicious behavior. Compared with gradient descent attacks, EvadeML improves the success rate of generating adversarial example and proves that machine …

Sciencedirect.com

DA: 21 PA: 38 MOZ Rank: 64

【AIセキュリティ超入門】第2話 – AIを騙す攻撃 – 敵対的サンプ …

evademl.org. EvadeML. https://evademl.org. 画像をぼかして必要最低限の情報のみにするなどの対策を行うことによって、攻撃者がAdversarial Examplesをある程度作成しづらいAIを作成することができます。特徴量絞り込みの例出典：Detecting Adversarial Examples in Deep Neural Networks. ネットワークの蒸留. 参考文献 ...

Ainow.ai

DA: 8 PA: 19 MOZ Rank: 33

EvadeRL:EvadingPDFMalwareClassifierswithDeep …

Research Article EvadeRL:EvadingPDFMalwareClassifierswithDeep ReinforcementLearning ZhengyangMao ,1 ZhiyangFang ,2 MeijinLi ,1 andYangFan 1 1College of Software ...

Downloads.hindawi.com

DA: 21 PA: 30 MOZ Rank: 58

逃逸机器学习的安全检测——evadeML、malGAN、deep-pwning …

逃逸机器学习的安全检测——evadeML、malGAN、deep-pwning、foolbox、Gym-Malware，攻击机器学习探测器：最先进的审查15:37机器学习（ML）是检测恶意软件的好方法。它在技术社区和科学界广泛使用，但有两种不同的观点：性能VS鲁棒性。技术界试图提高ML性能，以便大规模地提高可用性，同时科学界 ...

Blog.51cto.com

DA: 14 PA: 19 MOZ Rank: 41

Improving Robustness of ML Classifiers against Realizable

Realizable attack: EvadeML (Xu et al., NDSS). Automatically evades a PDF classi er by using genetic programming. Works on both structure- and content-based detectors. Feature-space attack model: multi-objective optimization. The modi ed feature vector is predicted as benign as possible. The modi cation cost (measured with an ‘ p norm) is minimized. General defense: …

Usenix.org

DA: 14 PA: 50 MOZ Rank: 92

Papers with Code - Feature Squeezing Mitigates and Detects …

Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples. Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier methods for generating adversarial examples. In this short note, we report on recent results ...

Paperswithcode.com

DA: 18 PA: 46 MOZ Rank: 74

EvadePDF: Towards Evading Machine Learning Based PDF Malware ...

A Brief Review of EvadeML: EvadeML is a Genetic Programming based algorithm. Genetic Programming techniques work by starting with an initial population set and introducing changes in each iteration to the population set and selecting a subset of the population. This shows promising scores (a higher fitness value) until the desired sample is found (with fitness …

Link.springer.com

DA: 17 PA: 37 MOZ Rank: 65

Table 3 | EvadeRL: Evading PDF Malware Classifiers with Deep

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles..

Hindawi.com

DA: 15 PA: 32 MOZ Rank: 59

对抗样本（对抗攻击）入门_小刘同学_的博客-CSDN博客_对抗样本 …

本文总结了近年提出的各种对抗样本的攻击方法。对抗样本的攻击主要分为无特定目标攻击（即只要求分类器对对抗样本错误分类，而不特定要求错误分类到哪一类）和特定目标攻击（即要求分类器将对抗样本错误分类到特定类别）。本文先介绍无特定目标攻击的目标函数，然后介绍fgsm、bim、stepll ...

Blog.csdn.net

DA: 13 PA: 38 MOZ Rank: 64

Python cv2.fastNlMeansDenoisingColored方法代码示例 - 纯净天空

Python cv2.fastNlMeansDenoisingColored使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。. 您也可以进一步了解该方法所在类cv2 的用法示例。. 在下文中一共展示了 cv2.fastNlMeansDenoisingColored方法的1个代码示例，这些例子默认根据受欢迎程度排序 ...

Vimsky.com

DA: 10 PA: 50 MOZ Rank: 91

Improving Robustness of ML Classifiers against Realizable

Ing EvadeML as a realizable attack [44]. Speciﬁcally, we consider four ML-based approaches for PDF malware detec-tion: two based on features that capture PDF ﬁle structure (SL2013 [36] and Hidost [38]), and two based on PDF ﬁle content (two Mimicus variants of PDFRate [35,37]). In all cases, we show that successful defense against a given ...

Usenix.org

DA: 14 PA: 28 MOZ Rank: 57

Python keras.backend 模块，is_keras_tensor() 实例源码 - 编程字典

Keras.backend. 模块，. is_keras_tensor () 实例源码. 我们从Python开源项目中，提取了以下 8 个代码示例，用于说明如何使用 keras.backend.is_keras_tensor () 。. 项目： AerialCrackDetection_Keras 作者： TTMRonald | 项目源码 | 文件源码. 项目： AerialCrackDetection_Keras 作者： TTMRonald | 项目 ...

Codingdict.com

DA: 14 PA: 36 MOZ Rank: 66

Jefferson's Wheel » Machine Learning

The talk focuses on work with Weilin Xu and Yanjun Qi on automatically evading malware classifiers using techniques from genetic programming. (See EvadeML.org for more details and links to code and papers, although some of the work I talked about at Enigma has not yet been published.). Enigma was an amazing conference – one of the most worthwhile, and definitely …

Uvasrg.github.io

DA: 16 PA: 31 MOZ Rank: 64

Python cv2.fastNlMeansDenoising方法代码示例 - 纯净天空

在下文中一共展示了cv2.fastNlMeansDenoising方法的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

Vimsky.com

DA: 10 PA: 50 MOZ Rank: 88

Generative 모델을활용한 멀웨어탐지블랙박스모델의 취약성분석

•To overcome the limitations of EvadeML, we employ a generative ML model that can automatically generate adversarial examples. •By learning the structures of both benign and malicious PDFs, the model aims to simultaneously achieve two goals: evading classifier and maintaining maliciousness. Evading classifier Maintaining maliciousness •The generator …

Journal-home.s3.ap-northeast-2.amazonaws.com

DA: 44 PA: 50 MOZ Rank: 31

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