ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples
<p dir="ltr">From recent research work, it has been shown that neural network (NN) classifiers are vulnerable to adversarial examples which contain special perturbations that are ignored by human eyes while can mislead NN classifiers. In this paper, we propose a practical black-box a...
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2020
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| _version_ | 1864513511408795648 |
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| author | Guanxiong Liu (2104315) |
| author2 | Issa Khalil (16855449) Abdallah Khreishah (16855455) Abdulelah Algosaibi (18973903) Adel Aldalbahi (18973906) Mohammed Alnaeem (18973909) Abdulaziz Alhumam (18973912) Muhammad Anan (18973915) |
| author2_role | author author author author author author author |
| author_facet | Guanxiong Liu (2104315) Issa Khalil (16855449) Abdallah Khreishah (16855455) Abdulelah Algosaibi (18973903) Adel Aldalbahi (18973906) Mohammed Alnaeem (18973909) Abdulaziz Alhumam (18973912) Muhammad Anan (18973915) |
| author_role | author |
| dc.creator.none.fl_str_mv | Guanxiong Liu (2104315) Issa Khalil (16855449) Abdallah Khreishah (16855455) Abdulelah Algosaibi (18973903) Adel Aldalbahi (18973906) Mohammed Alnaeem (18973909) Abdulaziz Alhumam (18973912) Muhammad Anan (18973915) |
| dc.date.none.fl_str_mv | 2020-10-07T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2020.3029270 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/ManiGen_A_Manifold_Aided_Black-Box_Generator_of_Adversarial_Examples/26176819 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Cybersecurity and privacy Data management and data science Machine learning Adversarial examples machine learning neural network manifold Generators Manifolds Artificial neural networks Optimization Perturbation methods Search problems |
| dc.title.none.fl_str_mv | ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">From recent research work, it has been shown that neural network (NN) classifiers are vulnerable to adversarial examples which contain special perturbations that are ignored by human eyes while can mislead NN classifiers. In this paper, we propose a practical black-box adversarial example generator, dubbed ManiGen. ManiGen does not require any knowledge of the inner state of the target classifier. It generates adversarial examples by searching along the manifold, which is a concise representation of input data. Through extensive set of experiments on different datasets, we show that (1) adversarial examples generated by ManiGen can mislead standalone classifiers by being as successful as the state-of-the-art white-box generator, Carlini, and (2) adversarial examples generated by ManiGen can more effectively attack classifiers with state-of-the-art defenses.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3029270" target="_blank">https://dx.doi.org/10.1109/access.2020.3029270</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_022e52cd2c3ab5ccccdfbfa0ee9c0ffa |
| identifier_str_mv | 10.1109/access.2020.3029270 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26176819 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | ManiGen: A Manifold Aided Black-Box Generator of Adversarial ExamplesGuanxiong Liu (2104315)Issa Khalil (16855449)Abdallah Khreishah (16855455)Abdulelah Algosaibi (18973903)Adel Aldalbahi (18973906)Mohammed Alnaeem (18973909)Abdulaziz Alhumam (18973912)Muhammad Anan (18973915)Information and computing sciencesCybersecurity and privacyData management and data scienceMachine learningAdversarial examplesmachine learningneural networkmanifoldGeneratorsManifoldsArtificial neural networksOptimizationPerturbation methodsSearch problems<p dir="ltr">From recent research work, it has been shown that neural network (NN) classifiers are vulnerable to adversarial examples which contain special perturbations that are ignored by human eyes while can mislead NN classifiers. In this paper, we propose a practical black-box adversarial example generator, dubbed ManiGen. ManiGen does not require any knowledge of the inner state of the target classifier. It generates adversarial examples by searching along the manifold, which is a concise representation of input data. Through extensive set of experiments on different datasets, we show that (1) adversarial examples generated by ManiGen can mislead standalone classifiers by being as successful as the state-of-the-art white-box generator, Carlini, and (2) adversarial examples generated by ManiGen can more effectively attack classifiers with state-of-the-art defenses.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3029270" target="_blank">https://dx.doi.org/10.1109/access.2020.3029270</a></p>2020-10-07T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2020.3029270https://figshare.com/articles/journal_contribution/ManiGen_A_Manifold_Aided_Black-Box_Generator_of_Adversarial_Examples/26176819CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/261768192020-10-07T09:00:00Z |
| spellingShingle | ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples Guanxiong Liu (2104315) Information and computing sciences Cybersecurity and privacy Data management and data science Machine learning Adversarial examples machine learning neural network manifold Generators Manifolds Artificial neural networks Optimization Perturbation methods Search problems |
| status_str | publishedVersion |
| title | ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples |
| title_full | ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples |
| title_fullStr | ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples |
| title_full_unstemmed | ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples |
| title_short | ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples |
| title_sort | ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples |
| topic | Information and computing sciences Cybersecurity and privacy Data management and data science Machine learning Adversarial examples machine learning neural network manifold Generators Manifolds Artificial neural networks Optimization Perturbation methods Search problems |