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arxiv 1610.00768 v6 pith:UWQ2O4RL submitted 2016-10-03 cs.LG cs.CRstat.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
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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
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 6 Pith papers

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    RoAd-RL is a new benchmarking library for adversarial reinforcement learning that evaluates DQN, PPO, and SAC agents across 192 attack-defense configurations and finds substantial robustness variations plus cases wher...

  2. Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization

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    RL training disrupts gradient-based adversarial attacks by inducing unstable low-magnitude gradients that limit the effectiveness of methods like PGD within practical budgets.

  3. Discriminative Active Learning

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

  4. 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.

  5. 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.

  6. LLM-Safety Evaluations Lack Robustness

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    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.