A black-box attack method of machine learning algorithms based on quantum autoencoders
Currently, researchers have conducted extensive studies on adversarial attacks in the field of machine learning. With the development of quantum computing technology, quantum computing has provided new ideas and methods for implementing machine learning algorithms. Meanwhile, the issue of adversaria...
Uloženo v:
| Vydáno v: | Physica A Ročník 680; s. 131033 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
15.12.2025
|
| Témata: | |
| ISSN: | 0378-4371 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Currently, researchers have conducted extensive studies on adversarial attacks in the field of machine learning. With the development of quantum computing technology, quantum computing has provided new ideas and methods for implementing machine learning algorithms. Meanwhile, the issue of adversarial attacks in quantum machine learning has increasingly become a research hotspot. This paper proposes a new black-box attack method against quantum machine learning models based on a quantum autoencoder (QAE). The method first obtains a basic dataset through a small number of queries to the model, then expands this basic dataset to obtain a training dataset. The training dataset is used to train a surrogate model to generate adversarial examples, and then the transferability of the adversarial examples is utilized to launch attacks, ultimately achieving a black-box attack on the target model. Experiments show that the proposed method only requires 20 queries to the target model. Based on the results of these queries, the quantum autoencoder can be used to expand the basic dataset, and the accuracy of the surrogate model for attacking the target model is improved by 8% on the generated test set. Moreover, compared with the deep convolutional generative adversarial network (DCGAN) model, this method can achieve faster fitting. After training, the effectiveness of transfer based attacks on the surrogate model only decreases by less than 20% under strong perturbation conditions, and under certain conditions, the attack effect on the target model is stronger than that on the surrogate model itself. In addition, using the surrogate model to attack another quantum neural network model also achieves similar effects to those on the target model, thereby further verifying the universality of the proposed attack method.
•Proposes a quantum autoencoder-based black-box attack method effective with minimal queries.•Achieves high attack success with only a few queries by enhancing decision boundary similarity.•Quantum-generated data improves attack stealth and effectiveness over traditional methods.•Adversarial examples show strong transferability between quantum models under interference.•Attack method demonstrates broad applicability across diverse quantum neural networks. |
|---|---|
| AbstractList | Currently, researchers have conducted extensive studies on adversarial attacks in the field of machine learning. With the development of quantum computing technology, quantum computing has provided new ideas and methods for implementing machine learning algorithms. Meanwhile, the issue of adversarial attacks in quantum machine learning has increasingly become a research hotspot. This paper proposes a new black-box attack method against quantum machine learning models based on a quantum autoencoder (QAE). The method first obtains a basic dataset through a small number of queries to the model, then expands this basic dataset to obtain a training dataset. The training dataset is used to train a surrogate model to generate adversarial examples, and then the transferability of the adversarial examples is utilized to launch attacks, ultimately achieving a black-box attack on the target model. Experiments show that the proposed method only requires 20 queries to the target model. Based on the results of these queries, the quantum autoencoder can be used to expand the basic dataset, and the accuracy of the surrogate model for attacking the target model is improved by 8% on the generated test set. Moreover, compared with the deep convolutional generative adversarial network (DCGAN) model, this method can achieve faster fitting. After training, the effectiveness of transfer based attacks on the surrogate model only decreases by less than 20% under strong perturbation conditions, and under certain conditions, the attack effect on the target model is stronger than that on the surrogate model itself. In addition, using the surrogate model to attack another quantum neural network model also achieves similar effects to those on the target model, thereby further verifying the universality of the proposed attack method.
•Proposes a quantum autoencoder-based black-box attack method effective with minimal queries.•Achieves high attack success with only a few queries by enhancing decision boundary similarity.•Quantum-generated data improves attack stealth and effectiveness over traditional methods.•Adversarial examples show strong transferability between quantum models under interference.•Attack method demonstrates broad applicability across diverse quantum neural networks. |
| ArticleNumber | 131033 |
| Author | Zhao, Jiayu Zhang, Shibin Chang, Yan Yan, Lili Tan, Dong |
| Author_xml | – sequence: 1 givenname: Dong surname: Tan fullname: Tan, Dong – sequence: 2 givenname: Lili surname: Yan fullname: Yan, Lili email: yanlili@cuit.edu.cn – sequence: 3 givenname: Jiayu surname: Zhao fullname: Zhao, Jiayu – sequence: 4 givenname: Yan surname: Chang fullname: Chang, Yan – sequence: 5 givenname: Shibin surname: Zhang fullname: Zhang, Shibin |
| BookMark | eNp9kL1OwzAUhT0UibbwBCx-gQT_JHEyMFQVf1IlFmC1HPumcUnsYruIvj0pZWY6dzjf1dG3QDPnHSB0Q0lOCa1ud_m-P0aVM8LKnHJKOJ-hOeGizgou6CVaxLgjhFDB2Ry9r3A7KP2Rtf4bq5SmE4-Qem-w7_CodG8d4AFUcNZtsRq2PtjUjxG3KsJUcvjzoFw6jFgdkgenvYEQr9BFp4YI13-5RG8P96_rp2zz8vi8Xm0yzcoyZVAbUVFRG9pUXctVxbuOmQbKAoAVrDSiKduWm1Zw2lHDtIC6JKZQomiEKihfIn7-q4OPMUAn98GOKhwlJfKkQ-7krw550iHPOibq7kzBNO3LQpBR22k6GBtAJ2m8_Zf_AVEsbr4 |
| Cites_doi | 10.1103/PhysRevLett.117.130501 10.1103/PhysRevApplied.16.024051 10.1007/s10489-022-04175-y 10.1016/j.media.2019.101552 10.1145/3052973.3053009 10.1088/1367-2630/ab976f 10.1109/COMST.2020.2975048 10.1109/ICTC49870.2020.9289439 10.1007/s42484-021-00061-x 10.1109/TIFS.2020.3021899 10.1016/j.neucom.2019.08.083 10.1103/PhysRevA.101.062331 10.1038/s42254-021-00348-9 10.1103/PhysRevResearch.6.023020 10.1063/PT.3.4164 10.1109/SP.2017.49 10.3390/a18030156 10.1103/PhysRevLett.124.130502 10.1007/s10462-022-10188-3 10.1088/2058-9565/aa8072 10.1038/nature23474 10.1109/ICCV.2017.153 10.1007/s10462-021-10072-6 10.1145/3422622 10.1088/2058-9565/aada1f 10.1016/j.patcog.2018.07.023 10.1103/PhysRevResearch.2.033212 10.1103/PhysRevA.101.032308 10.1038/s41567-019-0648-8 10.1109/TIFS.2023.3307908 |
| ContentType | Journal Article |
| Copyright | 2025 Elsevier B.V. |
| Copyright_xml | – notice: 2025 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.physa.2025.131033 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics |
| ExternalDocumentID | 10_1016_j_physa_2025_131033 S0378437125006855 |
| GroupedDBID | --K --M -DZ -~X .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 7-5 71M 8P~ 9JN 9JO AABNK AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAPFB AAQFI AATTM AAXKI AAXUO AAYWO ABAOU ABJNI ABMAC ABNEU ACDAQ ACFVG ACGFS ACLOT ACNCT ACRLP ADBBV ADEZE ADFHU ADGUI AEBSH AEIPS AEKER AEYQN AFFNX AFJKZ AFTJW AGHFR AGTHC AGUBO AGYEJ AHHHB AIEXJ AIGVJ AIIAU AIIUN AIKHN AITUG AIVDX ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU APXCP ARUGR AXJTR AXLSJ BKOJK BLXMC EBS EFJIC EFKBS EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W K-O KOM M38 M41 MHUIS MO0 N9A O-L O9- OAUVE OGIMB OZT P-8 P-9 P2P PC. Q38 RNS ROL RPZ SDF SDG SDP SES SEW SPC SPCBC SPD SSB SSF SSQ SSW SSZ T5K TN5 TWZ WH7 XPP YNT ZMT ~02 ~G- ~HD 29O 5VS 6TJ 9DU AAFFL AAQXK AAYXX ABFNM ABWVN ABXDB ACNNM ACROA ACRPL ADMUD ADNMO ADVLN AFODL AGQPQ AJWLA ASPBG AVWKF AZFZN BBWZM BEHZQ BEZPJ BGSCR BNTGB BPUDD BULVW BZJEE CITATION EJD FEDTE FGOYB HMV HVGLF HZ~ MVM NDZJH R2- SPG VOH WUQ XOL YYP ZY4 |
| ID | FETCH-LOGICAL-c255t-e8d76178d196fb3a63ff2d9e54ee2425d795bb3db731f1d2c7e850d4a7497a413 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001598760300002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0378-4371 |
| IngestDate | Sat Nov 29 06:51:27 EST 2025 Sat Nov 29 17:06:35 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Quantum machine learning Quantum autoencoders Adversarial samples Few queries |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c255t-e8d76178d196fb3a63ff2d9e54ee2425d795bb3db731f1d2c7e850d4a7497a413 |
| ParticipantIDs | crossref_primary_10_1016_j_physa_2025_131033 elsevier_sciencedirect_doi_10_1016_j_physa_2025_131033 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-12-15 |
| PublicationDateYYYYMMDD | 2025-12-15 |
| PublicationDate_xml | – month: 12 year: 2025 text: 2025-12-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationTitle | Physica A |
| PublicationYear | 2025 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Schuld, Bocharov, Svore, Wiebe (b45) 2020; 101 C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, R. Fergus, Intriguing properties of neural networks, in: 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings, 2014. Biggio, Roli (b8) 2018; 84 A. Radford, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, in: 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings, 2016. Q. Niyaz, W. Sun, A. Y. Javaid, M. Alam, A deep learning approach for network intrusion detection system, in: EAI International Conference on Bio-Inspired Information and Communications Technologies, BICT, 2015. S. Oh, J. Choi, J. Kim, A Tutorial on Quantum Convolutional Neural Networks (QCNN), in: International Conference on ICT Convergence, 2020, pp. 236–239. Chakraborty, Alam, Dey, Chattopadhyay, Mukhopadhyay (b7) 2018 N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, A. Swami, Practical black-box attacks against machine learning, in: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, 2017. Gong, Yuan, Li, Deng (b29) 2024; 6 Zhang, Avrithis, Furon, Amsaleg (b16) 2021; 16 Rebentrost, Mohseni, Lloyd (b47) 2014; 113 Li, Lu, Deng (b6) 2022 Biamonte, Wittek, Pancotti, Rebentrost, Wiebe, Lloyd (b1) 2017; 549 Cerezo, Arrasmith, Babbush, Benjamin, Endo, Fujii, McClean, Mitarai, Yuan, Cincio, Coles (b53) 2021; 3 Li, Ji, Han, Ji, Ren, Liu, Wu (b20) 2021; 18 Solorio-Fernández, Carrasco-Ochoa, Martínez-Trinidad (b37) 2022; 55 C. Xie, J. Wang, Z. Zhang, Y. Zhou, L. Xie, A. Yuille, Adversarial examples for semantic segmentation and object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017, pp. 1378–1387. N. Carlini, D. Wagner, Towards Evaluating the Robustness of Neural Networks, in: Proceedings - IEEE Symposium on Security and Privacy, 2017, pp. 39–57. Cong, Choi, Lukin (b32) 2019; 15 Li, Ma, Jiao (b39) 2015; 9 Qayyum, Usama, Qadir, Al-Fuqaha (b11) 2020; 22 Garcia-Cuesta, Aler, del Pozo-Vazquez, Galvan (b50) 2023; 53 Hur, Kim, Park (b33) 2022; 4 Liu, Wittek (b22) 2020; 101 Lu, Duan, Deng (b23) 2020; 2 Du, Hsieh, Liu, Tao, Liu (b26) 2020 Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio (b30) 2020; 63 A. Madry, A. Makelov, L. Schmidt, D. Tsipras, A. Vladu, Towards deep learning models resistant to adversarial attacks, in: Proceedings of the International Conference on Learning Representations, ICLR, 2018. A. Kurakin, I. J. Goodfellow, S. Bengio, Adversarial examples in the physical world, in: 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings, 2017. Deng, Li, Das Sarma (b46) 2017; 7 Liu, Wang, Yang (b38) 2019; 369 Li, Wooldridge, Wang (b28) 2023; vol. 448 Kerenidis, Prakash (b48) 2016 Miller, Xiang, Kesidis (b9) 2019 Huang, Du, Gong, Zhao, Wu, Wang, Li, Liang, Lin, Xu, Yang, Liu, Hsieh, Deng, Rong, Peng, Lu, Chen, Tao, Zhu, Pan (b49) 2021; 16 Silver, Patel, Tiwari (b51) 2022; vol. 36 Eyas, Engstrom, Athalye, Lin (b18) 2018; vol. 5 Talpur, Abdulkadir, Alhussian, Hasan, Aziz, Bamhdi (b36) 2023; 56 D.P. Kingma, M. Welling, Auto-encoding variational bayes, in: 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings, 2014. Romero, Olson, Aspuru-Guzik (b43) 2017; 2 Bondarenko, Feldmann, Polina (b44) 2020; 124 Khoshaman, Vinci, Denis, Andriyash, Amin (b42) 2019; 4 Chow (b52) 2025; 18 Casares, Martin-Delgado (b27) 2020; 22 Na, Ji, Kim (b19) 2023; vol. 13801 Dunjko, Taylor, Briegel (b2) 2016; 117 Yi, Walia, Babyn (b12) 2019; 58 Chen, Li, Wu, Ding, Zhang (b21) 2023; 18 Sarma, Deng, Duan (b4) 2019; 72 Gong, Deng (b24) 2022; 9 Qiu (b25) 2023; 4 Bausch (b3) 2020; vol. 33 Khodr, Younes (b35) 2011; vol. 4 Bausch (10.1016/j.physa.2025.131033_b3) 2020; vol. 33 Goodfellow (10.1016/j.physa.2025.131033_b30) 2020; 63 Khodr (10.1016/j.physa.2025.131033_b35) 2011; vol. 4 Liu (10.1016/j.physa.2025.131033_b22) 2020; 101 Romero (10.1016/j.physa.2025.131033_b43) 2017; 2 Chakraborty (10.1016/j.physa.2025.131033_b7) 2018 Gong (10.1016/j.physa.2025.131033_b29) 2024; 6 Biggio (10.1016/j.physa.2025.131033_b8) 2018; 84 Na (10.1016/j.physa.2025.131033_b19) 2023; vol. 13801 Li (10.1016/j.physa.2025.131033_b39) 2015; 9 Zhang (10.1016/j.physa.2025.131033_b16) 2021; 16 Sarma (10.1016/j.physa.2025.131033_b4) 2019; 72 Miller (10.1016/j.physa.2025.131033_b9) 2019 Chen (10.1016/j.physa.2025.131033_b21) 2023; 18 Schuld (10.1016/j.physa.2025.131033_b45) 2020; 101 Solorio-Fernández (10.1016/j.physa.2025.131033_b37) 2022; 55 Li (10.1016/j.physa.2025.131033_b28) 2023; vol. 448 Kerenidis (10.1016/j.physa.2025.131033_b48) 2016 Qiu (10.1016/j.physa.2025.131033_b25) 2023; 4 Li (10.1016/j.physa.2025.131033_b6) 2022 Du (10.1016/j.physa.2025.131033_b26) 2020 Rebentrost (10.1016/j.physa.2025.131033_b47) 2014; 113 10.1016/j.physa.2025.131033_b5 10.1016/j.physa.2025.131033_b34 10.1016/j.physa.2025.131033_b31 10.1016/j.physa.2025.131033_b41 10.1016/j.physa.2025.131033_b40 Yi (10.1016/j.physa.2025.131033_b12) 2019; 58 Eyas (10.1016/j.physa.2025.131033_b18) 2018; vol. 5 Cong (10.1016/j.physa.2025.131033_b32) 2019; 15 Biamonte (10.1016/j.physa.2025.131033_b1) 2017; 549 Casares (10.1016/j.physa.2025.131033_b27) 2020; 22 Huang (10.1016/j.physa.2025.131033_b49) 2021; 16 Silver (10.1016/j.physa.2025.131033_b51) 2022; vol. 36 Qayyum (10.1016/j.physa.2025.131033_b11) 2020; 22 Deng (10.1016/j.physa.2025.131033_b46) 2017; 7 Dunjko (10.1016/j.physa.2025.131033_b2) 2016; 117 Talpur (10.1016/j.physa.2025.131033_b36) 2023; 56 Gong (10.1016/j.physa.2025.131033_b24) 2022; 9 Lu (10.1016/j.physa.2025.131033_b23) 2020; 2 Hur (10.1016/j.physa.2025.131033_b33) 2022; 4 Khoshaman (10.1016/j.physa.2025.131033_b42) 2019; 4 Garcia-Cuesta (10.1016/j.physa.2025.131033_b50) 2023; 53 Li (10.1016/j.physa.2025.131033_b20) 2021; 18 Chow (10.1016/j.physa.2025.131033_b52) 2025; 18 Liu (10.1016/j.physa.2025.131033_b38) 2019; 369 10.1016/j.physa.2025.131033_b17 10.1016/j.physa.2025.131033_b15 10.1016/j.physa.2025.131033_b14 10.1016/j.physa.2025.131033_b13 Bondarenko (10.1016/j.physa.2025.131033_b44) 2020; 124 10.1016/j.physa.2025.131033_b10 Cerezo (10.1016/j.physa.2025.131033_b53) 2021; 3 |
| References_xml | – volume: vol. 5 start-page: 3392 year: 2018 end-page: 3401 ident: b18 article-title: Black-box adversarial attacks with limited queries and information publication-title: 35th International Conference on Machine Learning – volume: 16 start-page: 701 year: 2021 end-page: 713 ident: b16 article-title: Walking on the edge: Fast, low-distortion adversarial examples publication-title: IEEE Trans. Inf. Forensics Secur. – reference: S. Oh, J. Choi, J. Kim, A Tutorial on Quantum Convolutional Neural Networks (QCNN), in: International Conference on ICT Convergence, 2020, pp. 236–239. – volume: 113 year: 2014 ident: b47 article-title: Quantum support vector machine for big data classification publication-title: Phys. Rev. Lett. – volume: 4 year: 2023 ident: b25 article-title: Universal adversarial perturbations for multiple classification tasks with quantum classifiers publication-title: Mach. Learn.: Sci. Technol. – volume: 9 year: 2022 ident: b24 article-title: Universal adversarial examples and perturbations for quantum classifiers publication-title: Natl. Sci. Rev. – reference: C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, R. Fergus, Intriguing properties of neural networks, in: 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings, 2014. – volume: 549 start-page: 195 year: 2017 end-page: 202 ident: b1 article-title: Quantum machine learning publication-title: Nature – volume: 6 year: 2024 ident: b29 article-title: Enhancing quantum adversarial robustness by randomized encodings publication-title: Phys. Rev. Res. – volume: vol. 36 start-page: 8324 year: 2022 end-page: 8332 ident: b51 article-title: QUILT: Effective multi-class classification on quantum computers using an ensemble of diverse quantum classifiers publication-title: Proceedings of the 36th AAAI Conference on Artificial Intelligence – volume: 22 year: 2020 ident: b27 article-title: A quantum active learning algorithm for sampling against adversarial attacks publication-title: New J. Phys. – volume: 15 start-page: 1273 year: 2019 end-page: 1278 ident: b32 article-title: Quantum convolutional neural networks publication-title: Nat. Phys. – volume: 7 year: 2017 ident: b46 article-title: Quantum entanglement in neural network states publication-title: Phys. Rev. X – volume: vol. 13801 start-page: 467 year: 2023 end-page: 482 ident: b19 article-title: Unrestricted black-box adversarial attack using GAN with limited queries publication-title: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) – volume: 101 year: 2020 ident: b22 article-title: Vulnerability of quantum classification to adversarial perturbations publication-title: Phys. Rev. A – volume: 4 year: 2019 ident: b42 article-title: Quantum variational autoencoder publication-title: Quantum Sci. Technol. – year: 2022 ident: b6 article-title: Quantum neural network classifiers: A tutorial – volume: 56 start-page: 865 year: 2023 end-page: 913 ident: b36 article-title: Deep neuro-fuzzy system application trends, challenges, and future perspectives: a systematic survey publication-title: Artif. Intell. Rev. – volume: 55 start-page: 2821 year: 2022 end-page: 2846 ident: b37 article-title: A survey on feature selection methods for mixed data publication-title: Artif. Intell. Rev. – volume: 2 year: 2017 ident: b43 article-title: Quantum autoencoders for efficient compression of quantum data publication-title: Quantum Sci. Technol. – year: 2020 ident: b26 article-title: Quantum noise protects quantum classifiers against adversaries – volume: vol. 33 start-page: 1368 year: 2020 end-page: 1369 ident: b3 article-title: Recurrent quantum neural networks publication-title: Advances in Neural Information Processing Systems – volume: vol. 4 start-page: 1875 year: 2011 end-page: 1883 ident: b35 article-title: Dimensionality reduction on hyperspectral images: A comparative review based on artificial datas publication-title: Proceedings - 4th International Congress on Image and Signal Processing, CISP 2011 – volume: 16 year: 2021 ident: b49 article-title: Experimental quantum generative adversarial networks for image generation publication-title: Phys. Rev. Appl. – volume: 117 year: 2016 ident: b2 article-title: Quantum-enhanced machine learning publication-title: Phys. Rev. Lett. – volume: 58 year: 2019 ident: b12 article-title: Generative adversarial network in medical imaging: A review publication-title: Med. Image Anal. – reference: N. Carlini, D. Wagner, Towards Evaluating the Robustness of Neural Networks, in: Proceedings - IEEE Symposium on Security and Privacy, 2017, pp. 39–57. – volume: 4 year: 2022 ident: b33 article-title: Quantum convolutional neural network for classical data classification publication-title: Quantum Mach. Intell. – year: 2018 ident: b7 article-title: Adversarial attacks and defences: A survey – volume: 63 start-page: 139 year: 2020 end-page: 144 ident: b30 article-title: Generative adversarial networks publication-title: Commun. ACM – volume: 3 start-page: 625 year: 2021 end-page: 644 ident: b53 article-title: Variational quantum algorithms publication-title: Nat. Rev. Phys. – reference: Q. Niyaz, W. Sun, A. Y. Javaid, M. Alam, A deep learning approach for network intrusion detection system, in: EAI International Conference on Bio-Inspired Information and Communications Technologies, BICT, 2015. – volume: 22 start-page: 998 year: 2020 end-page: 1026 ident: b11 article-title: Securing connected & autonomous vehicles: Challenges posed by adversarial machine learning and the way forward publication-title: IEEE Commun. Surv. Tutorials – volume: 101 year: 2020 ident: b45 article-title: Circuit-centric quantum classifiers publication-title: Phys. Rev. A – volume: 369 start-page: 122 year: 2019 end-page: 133 ident: b38 article-title: Sparse autoencoder for social image understanding publication-title: Neurocomputing – volume: 18 start-page: 156 year: 2025 ident: b52 article-title: Quantum computing and machine learning in medical decision-making: A comprehensive review publication-title: Algorithms – reference: N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, A. Swami, Practical black-box attacks against machine learning, in: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, 2017. – reference: A. Kurakin, I. J. Goodfellow, S. Bengio, Adversarial examples in the physical world, in: 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings, 2017. – reference: A. Radford, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, in: 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings, 2016. – volume: 9 start-page: 205 year: 2015 end-page: 216 ident: b39 article-title: A hybrid malicious code detection method based on deep learning publication-title: Int. J. Secur. Appl. – volume: 84 start-page: 317 year: 2018 end-page: 331 ident: b8 article-title: Wild patterns: Ten years after the rise of adversarial machine learning publication-title: Pattern Recognit. – reference: A. Madry, A. Makelov, L. Schmidt, D. Tsipras, A. Vladu, Towards deep learning models resistant to adversarial attacks, in: Proceedings of the International Conference on Learning Representations, ICLR, 2018. – volume: 18 start-page: 1933 year: 2021 end-page: 1949 ident: b20 article-title: Adversarial examples versus cloud-based detectors: A black-box empirical study publication-title: IEEE Trans. Dependable Secur. Comput. – year: 2019 ident: b9 article-title: Adversarial learning in statistical classification: A comprehensive review of defenses against attacks – reference: D.P. Kingma, M. Welling, Auto-encoding variational bayes, in: 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings, 2014. – volume: 18 start-page: 5522 year: 2023 end-page: 5536 ident: b21 article-title: Query-efficient decision-based black-box patch attack publication-title: IEEE Trans. Inf. Forensics Secur. – volume: 124 year: 2020 ident: b44 article-title: Quantum autoencoders to denoise quantum data publication-title: Phys. Rev. Lett. – volume: vol. 448 year: 2023 ident: b28 article-title: Transferability of quantum adversarial machine learning publication-title: Proceedings of Seventh International Congress on Information and Communication Technology – volume: 72 start-page: 48 year: 2019 end-page: 54 ident: b4 article-title: Machine learning meets quantum physics publication-title: Phys. Today – volume: 53 start-page: 13053 year: 2023 end-page: 13066 ident: b50 article-title: A combination of supervised dimensionality reduction and learning methods to forecast solar radiation publication-title: Appl. Intell. – reference: C. Xie, J. Wang, Z. Zhang, Y. Zhou, L. Xie, A. Yuille, Adversarial examples for semantic segmentation and object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017, pp. 1378–1387. – volume: 2 year: 2020 ident: b23 article-title: Quantum adversarial machine learning publication-title: Phys. Rev. Res. – year: 2016 ident: b48 article-title: Quantum recommendation systems publication-title: Information Technology Convergence and Services – volume: 117 issue: 13 year: 2016 ident: 10.1016/j.physa.2025.131033_b2 article-title: Quantum-enhanced machine learning publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.117.130501 – volume: vol. 5 start-page: 3392 year: 2018 ident: 10.1016/j.physa.2025.131033_b18 article-title: Black-box adversarial attacks with limited queries and information – volume: 4 issue: 4 year: 2023 ident: 10.1016/j.physa.2025.131033_b25 article-title: Universal adversarial perturbations for multiple classification tasks with quantum classifiers publication-title: Mach. Learn.: Sci. Technol. – volume: 16 issue: 2 year: 2021 ident: 10.1016/j.physa.2025.131033_b49 article-title: Experimental quantum generative adversarial networks for image generation publication-title: Phys. Rev. Appl. doi: 10.1103/PhysRevApplied.16.024051 – year: 2020 ident: 10.1016/j.physa.2025.131033_b26 – volume: 53 start-page: 13053 issue: 11 year: 2023 ident: 10.1016/j.physa.2025.131033_b50 article-title: A combination of supervised dimensionality reduction and learning methods to forecast solar radiation publication-title: Appl. Intell. doi: 10.1007/s10489-022-04175-y – ident: 10.1016/j.physa.2025.131033_b31 – volume: 7 issue: 2 year: 2017 ident: 10.1016/j.physa.2025.131033_b46 article-title: Quantum entanglement in neural network states publication-title: Phys. Rev. X – volume: 58 year: 2019 ident: 10.1016/j.physa.2025.131033_b12 article-title: Generative adversarial network in medical imaging: A review publication-title: Med. Image Anal. doi: 10.1016/j.media.2019.101552 – ident: 10.1016/j.physa.2025.131033_b17 doi: 10.1145/3052973.3053009 – volume: 22 issue: 7 year: 2020 ident: 10.1016/j.physa.2025.131033_b27 article-title: A quantum active learning algorithm for sampling against adversarial attacks publication-title: New J. Phys. doi: 10.1088/1367-2630/ab976f – volume: vol. 13801 start-page: 467 year: 2023 ident: 10.1016/j.physa.2025.131033_b19 article-title: Unrestricted black-box adversarial attack using GAN with limited queries – ident: 10.1016/j.physa.2025.131033_b41 – volume: 22 start-page: 998 issue: 2 year: 2020 ident: 10.1016/j.physa.2025.131033_b11 article-title: Securing connected & autonomous vehicles: Challenges posed by adversarial machine learning and the way forward publication-title: IEEE Commun. Surv. Tutorials doi: 10.1109/COMST.2020.2975048 – ident: 10.1016/j.physa.2025.131033_b5 doi: 10.1109/ICTC49870.2020.9289439 – volume: 9 issue: 6 year: 2022 ident: 10.1016/j.physa.2025.131033_b24 article-title: Universal adversarial examples and perturbations for quantum classifiers publication-title: Natl. Sci. Rev. – volume: 4 issue: 1 year: 2022 ident: 10.1016/j.physa.2025.131033_b33 article-title: Quantum convolutional neural network for classical data classification publication-title: Quantum Mach. Intell. doi: 10.1007/s42484-021-00061-x – volume: 16 start-page: 701 year: 2021 ident: 10.1016/j.physa.2025.131033_b16 article-title: Walking on the edge: Fast, low-distortion adversarial examples publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2020.3021899 – volume: 369 start-page: 122 year: 2019 ident: 10.1016/j.physa.2025.131033_b38 article-title: Sparse autoencoder for social image understanding publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.08.083 – volume: 101 issue: 6 year: 2020 ident: 10.1016/j.physa.2025.131033_b22 article-title: Vulnerability of quantum classification to adversarial perturbations publication-title: Phys. Rev. A doi: 10.1103/PhysRevA.101.062331 – ident: 10.1016/j.physa.2025.131033_b34 – volume: 3 start-page: 625 issue: 9 year: 2021 ident: 10.1016/j.physa.2025.131033_b53 article-title: Variational quantum algorithms publication-title: Nat. Rev. Phys. doi: 10.1038/s42254-021-00348-9 – year: 2019 ident: 10.1016/j.physa.2025.131033_b9 – volume: 6 issue: 2 year: 2024 ident: 10.1016/j.physa.2025.131033_b29 article-title: Enhancing quantum adversarial robustness by randomized encodings publication-title: Phys. Rev. Res. doi: 10.1103/PhysRevResearch.6.023020 – ident: 10.1016/j.physa.2025.131033_b40 – volume: 72 start-page: 48 issue: 3 year: 2019 ident: 10.1016/j.physa.2025.131033_b4 article-title: Machine learning meets quantum physics publication-title: Phys. Today doi: 10.1063/PT.3.4164 – ident: 10.1016/j.physa.2025.131033_b15 doi: 10.1109/SP.2017.49 – volume: 18 start-page: 156 issue: 3 year: 2025 ident: 10.1016/j.physa.2025.131033_b52 article-title: Quantum computing and machine learning in medical decision-making: A comprehensive review publication-title: Algorithms doi: 10.3390/a18030156 – year: 2022 ident: 10.1016/j.physa.2025.131033_b6 – ident: 10.1016/j.physa.2025.131033_b10 – volume: 113 issue: 3 year: 2014 ident: 10.1016/j.physa.2025.131033_b47 article-title: Quantum support vector machine for big data classification publication-title: Phys. Rev. Lett. – volume: vol. 36 start-page: 8324 year: 2022 ident: 10.1016/j.physa.2025.131033_b51 article-title: QUILT: Effective multi-class classification on quantum computers using an ensemble of diverse quantum classifiers – volume: 124 issue: 13 year: 2020 ident: 10.1016/j.physa.2025.131033_b44 article-title: Quantum autoencoders to denoise quantum data publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.124.130502 – volume: vol. 33 start-page: 1368 year: 2020 ident: 10.1016/j.physa.2025.131033_b3 article-title: Recurrent quantum neural networks – ident: 10.1016/j.physa.2025.131033_b14 – volume: 18 start-page: 1933 issue: 4 year: 2021 ident: 10.1016/j.physa.2025.131033_b20 article-title: Adversarial examples versus cloud-based detectors: A black-box empirical study publication-title: IEEE Trans. Dependable Secur. Comput. – volume: 56 start-page: 865 issue: 2 year: 2023 ident: 10.1016/j.physa.2025.131033_b36 article-title: Deep neuro-fuzzy system application trends, challenges, and future perspectives: a systematic survey publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-022-10188-3 – volume: 2 issue: 4 year: 2017 ident: 10.1016/j.physa.2025.131033_b43 article-title: Quantum autoencoders for efficient compression of quantum data publication-title: Quantum Sci. Technol. doi: 10.1088/2058-9565/aa8072 – volume: 549 start-page: 195 issue: 7671 year: 2017 ident: 10.1016/j.physa.2025.131033_b1 article-title: Quantum machine learning publication-title: Nature doi: 10.1038/nature23474 – volume: vol. 448 year: 2023 ident: 10.1016/j.physa.2025.131033_b28 article-title: Transferability of quantum adversarial machine learning – ident: 10.1016/j.physa.2025.131033_b13 doi: 10.1109/ICCV.2017.153 – volume: vol. 4 start-page: 1875 year: 2011 ident: 10.1016/j.physa.2025.131033_b35 article-title: Dimensionality reduction on hyperspectral images: A comparative review based on artificial datas – year: 2018 ident: 10.1016/j.physa.2025.131033_b7 – volume: 55 start-page: 2821 issue: 4 year: 2022 ident: 10.1016/j.physa.2025.131033_b37 article-title: A survey on feature selection methods for mixed data publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-021-10072-6 – volume: 9 start-page: 205 issue: 5 year: 2015 ident: 10.1016/j.physa.2025.131033_b39 article-title: A hybrid malicious code detection method based on deep learning publication-title: Int. J. Secur. Appl. – volume: 63 start-page: 139 issue: 11 year: 2020 ident: 10.1016/j.physa.2025.131033_b30 article-title: Generative adversarial networks publication-title: Commun. ACM doi: 10.1145/3422622 – volume: 4 issue: 1 year: 2019 ident: 10.1016/j.physa.2025.131033_b42 article-title: Quantum variational autoencoder publication-title: Quantum Sci. Technol. doi: 10.1088/2058-9565/aada1f – volume: 84 start-page: 317 year: 2018 ident: 10.1016/j.physa.2025.131033_b8 article-title: Wild patterns: Ten years after the rise of adversarial machine learning publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2018.07.023 – volume: 2 issue: 3 year: 2020 ident: 10.1016/j.physa.2025.131033_b23 article-title: Quantum adversarial machine learning publication-title: Phys. Rev. Res. doi: 10.1103/PhysRevResearch.2.033212 – volume: 101 issue: 3 year: 2020 ident: 10.1016/j.physa.2025.131033_b45 article-title: Circuit-centric quantum classifiers publication-title: Phys. Rev. A doi: 10.1103/PhysRevA.101.032308 – volume: 15 start-page: 1273 issue: 12 year: 2019 ident: 10.1016/j.physa.2025.131033_b32 article-title: Quantum convolutional neural networks publication-title: Nat. Phys. doi: 10.1038/s41567-019-0648-8 – volume: 18 start-page: 5522 year: 2023 ident: 10.1016/j.physa.2025.131033_b21 article-title: Query-efficient decision-based black-box patch attack publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2023.3307908 – year: 2016 ident: 10.1016/j.physa.2025.131033_b48 article-title: Quantum recommendation systems |
| SSID | ssj0001732 |
| Score | 2.477687 |
| Snippet | Currently, researchers have conducted extensive studies on adversarial attacks in the field of machine learning. With the development of quantum computing... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 131033 |
| SubjectTerms | Adversarial samples Few queries Quantum autoencoders Quantum machine learning |
| Title | A black-box attack method of machine learning algorithms based on quantum autoencoders |
| URI | https://dx.doi.org/10.1016/j.physa.2025.131033 |
| Volume | 680 |
| WOSCitedRecordID | wos001598760300002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0378-4371 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0001732 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3da9RAEF-0VeiL-In1i33w7Uy5bHazyeOhldaHUvCU8ynsZjc1pSbtXU7O_96ZzObu4ERU8CWEQDZhfst87cz8GHs99hD3OFVGlTAqksJnkZE2jkprnC1VDhba9mQT-uwsm83y80Bzt-jpBHTTZKtVfv1foYZnADa2zv4F3OtF4QHcA-hwBdjh-kfAT0YWk3KRbVcj03VwG2ii-6P0vnbSD2QRFyNzddHO6-7rt8UIDZrDw4ObJYgbi5aXXYtzLl2okh-c2HPCdpMGnVIW9V0bzCBqkdDTUF_VW7lpOuapzY_lVlkBaZsvYZuGDIRQWM1BPZiUFttpjaF2LAhPZUL8KoOqTYm1aUdtUwbh8gizOTgMSqijGAnQko2VWtcOfsSVcWFw3sZpptRtti-0ykGl7U9Oj2cf1oY41gkdIoU_GYZO9eV9O5_6tWOy5WxM77N7IUrgE0L3Abvlm4fsLsl98Yh9nvA1xpww5oQxbyseMOYDxnyDMe8x5m3DA8Z8G-PH7NP74-nbkygQZEQlRIJd5DOnscXTgRqtbGLSpKqEy72S3mMo6XSurMUJ2klcxU6U2mdq7KTRMtcG3JcnbK9pG_-UcS18aqWojJC2Z50XcSlBfiZ1zonEH7I3g3SKa5qDUgwFgpdFL8wChVmQMA9ZOkiwCK4cuWgFQP67F5_964vP2cFmb75ge9186V-yO-X3rl7MX4Wt8RN4aG6o |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+black-box+attack+method+of+machine+learning+algorithms+based+on+quantum+autoencoders&rft.jtitle=Physica+A&rft.au=Tan%2C+Dong&rft.au=Yan%2C+Lili&rft.au=Zhao%2C+Jiayu&rft.au=Chang%2C+Yan&rft.date=2025-12-15&rft.pub=Elsevier+B.V&rft.issn=0378-4371&rft.volume=680&rft_id=info:doi/10.1016%2Fj.physa.2025.131033&rft.externalDocID=S0378437125006855 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0378-4371&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0378-4371&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0378-4371&client=summon |