Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions
Deep neural network (DNN) classifiers are potent instruments that can be used in various security-sensitive applications. Nonetheless, they are vulnerable to certain attacks that impede or distort their learning process. For example, backdoor attacks involve polluting the DNN learning set with a few...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10403914/ |
| _version_ | 1797292008873656320 |
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| author | Orson Mengara Anderson Avila Tiago H. Falk |
| author_facet | Orson Mengara Anderson Avila Tiago H. Falk |
| author_sort | Orson Mengara |
| collection | DOAJ |
| description | Deep neural network (DNN) classifiers are potent instruments that can be used in various security-sensitive applications. Nonetheless, they are vulnerable to certain attacks that impede or distort their learning process. For example, backdoor attacks involve polluting the DNN learning set with a few samples from one or more source classes, which are then labeled as target classes by an attacker. Even if the DNN is trained on clean samples with no backdoors, this attack will still be successful if a backdoor pattern exists in the training data. Backdoor attacks are difficult to spot and can be used to make the DNN behave maliciously, depending on the target selected by the attacker. In this study, we survey the literature and highlight the latest advances in backdoor attack strategies and defense mechanisms. We finalize the discussion on challenges and open issues, as well as future research opportunities. |
| first_indexed | 2024-03-07T19:46:02Z |
| format | Article |
| id | doaj.art-0bce14f96bb4413cb0a58f1a4773c0ac |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T19:46:02Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0bce14f96bb4413cb0a58f1a4773c0ac2024-02-29T00:00:34ZengIEEEIEEE Access2169-35362024-01-0112290042902310.1109/ACCESS.2024.335581610403914Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research DirectionsOrson Mengara0https://orcid.org/0009-0009-4022-3499Anderson Avila1Tiago H. Falk2https://orcid.org/0000-0002-5739-2514INRS-EMT, University of Quebec, Montreal, CanadaINRS-EMT, University of Quebec, Montreal, CanadaINRS-EMT, University of Quebec, Montreal, CanadaDeep neural network (DNN) classifiers are potent instruments that can be used in various security-sensitive applications. Nonetheless, they are vulnerable to certain attacks that impede or distort their learning process. For example, backdoor attacks involve polluting the DNN learning set with a few samples from one or more source classes, which are then labeled as target classes by an attacker. Even if the DNN is trained on clean samples with no backdoors, this attack will still be successful if a backdoor pattern exists in the training data. Backdoor attacks are difficult to spot and can be used to make the DNN behave maliciously, depending on the target selected by the attacker. In this study, we survey the literature and highlight the latest advances in backdoor attack strategies and defense mechanisms. We finalize the discussion on challenges and open issues, as well as future research opportunities.https://ieeexplore.ieee.org/document/10403914/Backdoor attackdeep learningvulnerability detectiontrojan attackneural trojan |
| spellingShingle | Orson Mengara Anderson Avila Tiago H. Falk Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions IEEE Access Backdoor attack deep learning vulnerability detection trojan attack neural trojan |
| title | Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions |
| title_full | Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions |
| title_fullStr | Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions |
| title_full_unstemmed | Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions |
| title_short | Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions |
| title_sort | backdoor attacks to deep neural networks a survey of the literature challenges and future research directions |
| topic | Backdoor attack deep learning vulnerability detection trojan attack neural trojan |
| url | https://ieeexplore.ieee.org/document/10403914/ |
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