Papers › Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy
Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset.
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Code
Syntology Ran 10 of 21 code samples harvested from 8 repositories linked to this paper; 11 have no recorded run. Of those that ran: 2 ran · honoured contract; 1 ran · violated contract; 6 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
21 samples harvested; 10 ran; 2 honoured the contract we drafted; 11 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | MNIST | Explaining and Harnessing Adversarial Examples | Percentage error | 0.8 | #46 of 81 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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