Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
Authors: Eric Wong, Zico Kolter
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the approach on a number of tasks to train classifiers with robust adversarial guarantees (e.g. for MNIST, we produce a convolutional classifier that provably has less than 5.8% test error for any adversarial attack with bounded ℓ norm less than ϵ = 0.1), and code for all experiments is available at http://github.com/ locuslab/convex_adversarial. |
| Researcher Affiliation | Academia | 1Machine Learning Department, Carnegie Mellon University, Pittsburgh PA, 15213, USA 2Computer Science Department, Carnegie Mellon University, Pittsburgh PA, 15213, USA. Correspondence to: Eric Wong <ericwong@cs.cmu.edu>, J. Zico Kolter <zkolter@cs.cmu.edu>. |
| Pseudocode | Yes | Algorithm 1 Computing Activation Bounds |
| Open Source Code | Yes | code for all experiments is available at http://github.com/ locuslab/convex_adversarial. |
| Open Datasets | Yes | We evaluate our approach on classification tasks such as human activity recognition, MNIST digit classification, Fashion MNIST , and street view housing numbers. ... Fashion-MNIST dataset (Xiao et al., 2017), a harder dataset with the same size (in dimension and number of examples) as MNIST and human activity recognition dataset (Anguita et al., 2013). |
| Dataset Splits | No | The paper mentions training and testing but does not provide specific details on train/validation/test splits (e.g., percentages or sample counts) for the datasets used. |
| Hardware Specification | Yes | All experiments were run on a single Titan X GPU. |
| Software Dependencies | No | The paper mentions using a standard stochastic gradient variant and autodiff toolkit but does not specify any software names with version numbers. |
| Experiment Setup | Yes | The final classifier after 100 epochs reaches a test error of 1.80% with a robust test error of 5.82%. |