MUTEN: Mutant-Based Ensembles for Boosting Gradient-Based Adversarial Attack
Mutation testing (MT) for deep learning (DL) has gained huge attention in the past few years. However, how MT can really help DL is still unclear. In this paper, we introduce one promising direction for the usage of mutants. Specifically, since mutants can be seen as one kind of ensemble model and e...
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| Vydáno v: | IEEE/ACM International Conference on Automated Software Engineering : [proceedings] s. 1708 - 1712 |
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IEEE
11.09.2023
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| ISSN: | 2643-1572 |
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| Abstract | Mutation testing (MT) for deep learning (DL) has gained huge attention in the past few years. However, how MT can really help DL is still unclear. In this paper, we introduce one promising direction for the usage of mutants. Specifically, since mutants can be seen as one kind of ensemble model and ensemble model can be used to boost the adversarial attack, we propose MUTEN, which applies the attack on mutants to improve the success rate of well-known attacks against gradient-masking models. Experimental results on MNIST, SVHN, and CIFAR-10 show that MUTEN can increase the success rate of four attacks by up to 45%. Furthermore, experiments on four defense approaches, bit-depth reduction, JPEG compression, Defensive distillation, and Label smoothing, demonstrate that MUTEN can break the defense models effectively by enhancing the attacks with the success rate of up to 96%. |
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| AbstractList | Mutation testing (MT) for deep learning (DL) has gained huge attention in the past few years. However, how MT can really help DL is still unclear. In this paper, we introduce one promising direction for the usage of mutants. Specifically, since mutants can be seen as one kind of ensemble model and ensemble model can be used to boost the adversarial attack, we propose MUTEN, which applies the attack on mutants to improve the success rate of well-known attacks against gradient-masking models. Experimental results on MNIST, SVHN, and CIFAR-10 show that MUTEN can increase the success rate of four attacks by up to 45%. Furthermore, experiments on four defense approaches, bit-depth reduction, JPEG compression, Defensive distillation, and Label smoothing, demonstrate that MUTEN can break the defense models effectively by enhancing the attacks with the success rate of up to 96%. |
| Author | Traon, Yves Le Guo, Yuejun Cordy, Maxime Papadakis, Mike Hu, Qiang |
| Author_xml | – sequence: 1 givenname: Qiang surname: Hu fullname: Hu, Qiang organization: University of Luxembourg,Luxembourg – sequence: 2 givenname: Yuejun surname: Guo fullname: Guo, Yuejun organization: Luxembourg Institute of Science and Technology,Luxembourg – sequence: 3 givenname: Maxime surname: Cordy fullname: Cordy, Maxime organization: University of Luxembourg,Luxembourg – sequence: 4 givenname: Mike surname: Papadakis fullname: Papadakis, Mike organization: University of Luxembourg,Luxembourg – sequence: 5 givenname: Yves Le surname: Traon fullname: Traon, Yves Le organization: University of Luxembourg,Luxembourg |
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| Snippet | Mutation testing (MT) for deep learning (DL) has gained huge attention in the past few years. However, how MT can really help DL is still unclear. In this... |
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| SubjectTerms | Boosting Deep learning Smoothing methods Software engineering Testing Transform coding |
| Title | MUTEN: Mutant-Based Ensembles for Boosting Gradient-Based Adversarial Attack |
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