{"url":"/method/accuracy-robustness-area-ara","slug":"accuracy-robustness-area-ara","name":"Accuracy-Robustness Area (ARA)","full_name":"Accuracy-Robustness Area","full_name_withheld":false,"description_markdown":"In the space of adversarial perturbation against classifier accuracy, the ARA is the area between a classifier's curve and the straight line defined by a naive classifier's maximum accuracy. Intuitively, the ARA measures a combination of the classifier’s predictive power and its ability to overcome an adversary. Importantly, when contrasted against existing robustness metrics, the ARA takes into account the classifier’s performance against all adversarial examples, without  bounding them by some arbitrary $\\epsilon$.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness","paper":"/paper/reliable-classification-explanations-via","first_author":"Walt Woods","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/reliable-classification-explanations-via"},"source":{"url":"https://arxiv.org/abs/1906.02896v2","title":"Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Adversarial Training","url":"/methods/category/adversarial-training","pwc_aliases":[]}],"n_papers_tagged":3,"archive_num_papers":3,"papers_newest_first":[{"paper":"/paper/adversarial-multi-task-underwater-acoustic","title":"Adversarial multi-task underwater acoustic target recognition: towards robustness against various influential factors","date":"2024-11-05","arxiv_id":"2411.02848","n_code_links":1,"syntology":null},{"paper":"/paper/reliable-classification-explanations-via","title":"Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness","date":"2019-06-07","arxiv_id":"1906.02896","n_code_links":1,"syntology":null},{"paper":"/paper/fake-news-detection-via-nlp-is-vulnerable-to","title":"Fake News Detection via NLP is Vulnerable to Adversarial Attacks","date":"2019-01-05","arxiv_id":"1901.09657","n_code_links":1,"syntology":null}],"papers_shown":3,"tasks":[{"task":"/task/adversarial-defense","name":"Adversarial Defense","papers":1},{"task":"/task/fact-checking","name":"Fact Checking","papers":1},{"task":"/task/fake-news-detection","name":"Fake News Detection","papers":1},{"task":"/task/fraud-detection","name":"Fraud Detection","papers":1},{"task":"/task/classification","name":"General Classification","papers":1},{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/interpretable-machine-learning","name":"Interpretable Machine Learning","papers":1},{"task":"/task/robust-classification","name":"Robust classification","papers":1},{"task":"/task/image-classification","name":"image-classification","papers":1}],"tasks_shown":9,"n_tasks":9,"usage_by_year":[{"year":"2019","papers":2},{"year":"2024","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/accuracy-robustness-area-ara"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}