# Image Classification on ImageNet (Adversarial Robustness) Source: [Image Classification on ImageNet (Adversarial Robustness)]() Collection index: [llms.txt]() Data updated: 2026-02-26T05:34:13.305Z Evaluation of Image Classification performance on the standard ImageNet dataset, measuring robustness against adversarial attacks (e.g., PGD, CW, Auto Attack). A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. | Scope | Recorded value | | --- | --- | | Task | Image Classification | | Dataset (including any stated split) | ImageNet | | Metric | Accuracy (Clean) | | Unit | PERCENT | | Direction | Higher is better | | Results loaded | 0 of 0 | | Coverage | All results currently returned for this benchmark | Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol. ## Metrics - [Accuracy (Clean)](): Higher is better; unit: PERCENT. - [Robust Accuracy (PGD)](): Higher is better; unit: PERCENT. - [Perturbation Magnitude (CW)](): Lower is better; unit: SCALAR. - [Robust Accuracy (Auto Attack)](): Higher is better; unit: PERCENT. ## Recorded results | Method | Accuracy (Clean) | Result date | Report paper | | --- | --- | --- | --- | Page 1 of 1. This is an index page, not the full collection.