Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study
<p dir="ltr">The use of artificial intelligence (AI) at the edge is transforming every aspect of the lives of human beings from scheduling daily activities to personalized shopping recommendations. Since the success of AI is to be measured ultimately in terms of how it benefits human...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2023
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إضافة وسم
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| _version_ | 1864513542561988608 |
|---|---|
| author | Muhammad Atif Butt (10849980) |
| author2 | Adnan Qayyum (16875936) Hassan Ali (3348749) Ala Al-Fuqaha (4434340) Junaid Qadir (16494902) |
| author2_role | author author author author |
| author_facet | Muhammad Atif Butt (10849980) Adnan Qayyum (16875936) Hassan Ali (3348749) Ala Al-Fuqaha (4434340) Junaid Qadir (16494902) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Atif Butt (10849980) Adnan Qayyum (16875936) Hassan Ali (3348749) Ala Al-Fuqaha (4434340) Junaid Qadir (16494902) |
| dc.date.none.fl_str_mv | 2023-02-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.cose.2022.103058 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Towards_secure_private_and_trustworthy_human-centric_embedded_machine_learning_An_emotion-aware_facial_recognition_case_study/24501046 |
| 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 Artificial intelligence Computer vision and multimedia computation Cybersecurity and privacy Human-centred computing Machine learning Embedded machine learning Human-Centered artificial intelligence Adversarial machine learning Privacy-awareness Trustworthiness Security Robustness Tiny machine learning |
| dc.title.none.fl_str_mv | Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The use of artificial intelligence (AI) at the edge is transforming every aspect of the lives of human beings from scheduling daily activities to personalized shopping recommendations. Since the success of AI is to be measured ultimately in terms of how it benefits human beings, and that the data driving the deep learning-based edge AI algorithms are intricately and intimately tied to humans, it is important to look at these AI technologies through a human-centric lens. However, despite the significant impact of AI design on human interests, the security and trustworthiness of edge AI applications are not foolproof and ethicalneither foolproof nor ethical; Moreover, social norms are often ignored duringin the design, implementation, and deployment of edge AI systems. In this paper, we make the following two contributions: Firstly, we analyze the application of edge AI through a human-centric perspective. More specifically, we present a pipeline to develop human-centric embedded machine learning (HC-EML) applications leveraging a generic human-centric AI (HCAI) framework. Alongside, we also analyzediscuss the privacy, trustworthiness, robustness, and security aspects of HC-EML applications with an insider look at their challenges and possible solutions along the way. Secondly, to illustrate the gravity of these issues, we present a case study on the task of human facial emotion recognition (FER) based on AffectNet dataset, where we analyze the effects of widely used input quantization on the security, robustness, fairness, and trustworthiness of an EML model. We find that input quantization partially degrades the efficacy of adversarial and backdoor attacks at the cost of a slight decrease in accuracy over clean inputs. By analyzing the explanations generated by SHAP, we identify that the decision of a FER model is largely influenced by features such as eyes, alar crease, lips, and jaws. Additionally, we note that input quantization is notably biased against the dark skin faces, and hypothesize that low-contrast features of dark skin faces may be responsible for the observed trends. We conclude with precautionary remarks and guidelines for future researchers.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers & Security<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.cose.2022.103058" target="_blank">https://dx.doi.org/10.1016/j.cose.2022.103058</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0e7f7ff56333a8203c0ab858e421cd99 |
| identifier_str_mv | 10.1016/j.cose.2022.103058 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24501046 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case studyMuhammad Atif Butt (10849980)Adnan Qayyum (16875936)Hassan Ali (3348749)Ala Al-Fuqaha (4434340)Junaid Qadir (16494902)Information and computing sciencesArtificial intelligenceComputer vision and multimedia computationCybersecurity and privacyHuman-centred computingMachine learningEmbedded machine learningHuman-Centered artificial intelligenceAdversarial machine learningPrivacy-awarenessTrustworthinessSecurityRobustnessTiny machine learning<p dir="ltr">The use of artificial intelligence (AI) at the edge is transforming every aspect of the lives of human beings from scheduling daily activities to personalized shopping recommendations. Since the success of AI is to be measured ultimately in terms of how it benefits human beings, and that the data driving the deep learning-based edge AI algorithms are intricately and intimately tied to humans, it is important to look at these AI technologies through a human-centric lens. However, despite the significant impact of AI design on human interests, the security and trustworthiness of edge AI applications are not foolproof and ethicalneither foolproof nor ethical; Moreover, social norms are often ignored duringin the design, implementation, and deployment of edge AI systems. In this paper, we make the following two contributions: Firstly, we analyze the application of edge AI through a human-centric perspective. More specifically, we present a pipeline to develop human-centric embedded machine learning (HC-EML) applications leveraging a generic human-centric AI (HCAI) framework. Alongside, we also analyzediscuss the privacy, trustworthiness, robustness, and security aspects of HC-EML applications with an insider look at their challenges and possible solutions along the way. Secondly, to illustrate the gravity of these issues, we present a case study on the task of human facial emotion recognition (FER) based on AffectNet dataset, where we analyze the effects of widely used input quantization on the security, robustness, fairness, and trustworthiness of an EML model. We find that input quantization partially degrades the efficacy of adversarial and backdoor attacks at the cost of a slight decrease in accuracy over clean inputs. By analyzing the explanations generated by SHAP, we identify that the decision of a FER model is largely influenced by features such as eyes, alar crease, lips, and jaws. Additionally, we note that input quantization is notably biased against the dark skin faces, and hypothesize that low-contrast features of dark skin faces may be responsible for the observed trends. We conclude with precautionary remarks and guidelines for future researchers.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers & Security<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.cose.2022.103058" target="_blank">https://dx.doi.org/10.1016/j.cose.2022.103058</a></p>2023-02-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.cose.2022.103058https://figshare.com/articles/journal_contribution/Towards_secure_private_and_trustworthy_human-centric_embedded_machine_learning_An_emotion-aware_facial_recognition_case_study/24501046CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/245010462023-02-01T00:00:00Z |
| spellingShingle | Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study Muhammad Atif Butt (10849980) Information and computing sciences Artificial intelligence Computer vision and multimedia computation Cybersecurity and privacy Human-centred computing Machine learning Embedded machine learning Human-Centered artificial intelligence Adversarial machine learning Privacy-awareness Trustworthiness Security Robustness Tiny machine learning |
| status_str | publishedVersion |
| title | Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study |
| title_full | Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study |
| title_fullStr | Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study |
| title_full_unstemmed | Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study |
| title_short | Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study |
| title_sort | Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study |
| topic | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Cybersecurity and privacy Human-centred computing Machine learning Embedded machine learning Human-Centered artificial intelligence Adversarial machine learning Privacy-awareness Trustworthiness Security Robustness Tiny machine learning |