pith. sign in

arxiv: 1610.00768 · v6 · pith:UWQ2O4RLnew · submitted 2016-10-03 · 💻 cs.LG · cs.CR· stat.ML

Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

Nicolas Papernot , Fartash Faghri , Nicholas Carlini , Ian Goodfellow , Reuben Feinman , Alexey Kurakin , Cihang Xie , Yash Sharma
show 18 more authors
Tom Brown Aurko Roy Alexander Matyasko Vahid Behzadan Karen Hambardzumyan Zhishuai Zhang Yi-Lin Juang Zhi Li Ryan Sheatsley Abhibhav Garg Jonathan Uesato Willi Gierke Yinpeng Dong David Berthelot Paul Hendricks Jonas Rauber Rujun Long Patrick McDaniel
This is my paper
classification 💻 cs.LG cs.CRstat.ML
keywords adversariallibrarysectioncleverhansconstructionexampleexampleslearning
0
0 comments X
read the original abstract

CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be used to develop more robust machine learning models and to provide standardized benchmarks of models' performance in the adversarial setting. Benchmarks constructed without a standardized implementation of adversarial example construction are not comparable to each other, because a good result may indicate a robust model or it may merely indicate a weak implementation of the adversarial example construction procedure. This technical report is structured as follows. Section 1 provides an overview of adversarial examples in machine learning and of the CleverHans software. Section 2 presents the core functionalities of the library: namely the attacks based on adversarial examples and defenses to improve the robustness of machine learning models to these attacks. Section 3 describes how to report benchmark results using the library. Section 4 describes the versioning system.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Discriminative Active Learning

    cs.LG 2019-07 unverdicted novelty 6.0

    DAL poses batch active learning as a binary classification task between labeled and unlabeled data to select informative examples for labeling.

  2. Latent Adversarial Defence with Boundary-guided Generation

    cs.LG 2019-07 unverdicted novelty 5.0

    LAD generates diverse adversarial examples in latent space by perturbing along normals to an SVM-defined decision boundary and uses them for adversarial training to improve DNN robustness.

  3. Defending Adversarial Attacks by Correcting logits

    cs.LG 2019-06 unverdicted novelty 5.0

    A two-layer network trained on mixed clean and perturbed logits recovers original predictions for a range of adversarial attacks without needing image data.

  4. LLM-Safety Evaluations Lack Robustness

    cs.CR 2025-03 unverdicted novelty 4.0

    LLM safety evaluations are hindered by noise in dataset curation, automated red-teaming, response generation, and LLM-judge evaluation, making fair comparisons difficult and slowing progress.