{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T15:22:18Z","timestamp":1758122538506,"version":"3.37.3"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T00:00:00Z","timestamp":1682726400000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin","doi-asserted-by":"crossref","award":["No. 19JCYBJC15300"],"award-info":[{"award-number":["No. 19JCYBJC15300"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Electron Test"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Hardware Trojans are usually implanted by making malicious changes to a\u00a0chip circuit, which can destroy chip functions or\u00a0expose sensitive information once activated. The hardware Trojan detection method based on side channel information has now become one of the most widely used detection methods. However, due to the influence of the deviation of the acquisition equipment and the noise of the actual chip working environment, insufficient acquisition of useful information of the collected side channel information occurs, affecting the final results. To address the problem, this paper proposes a detection method based on a dual discriminator assisted conditional generation adversarial network (D2ACGAN), which combines the benefits of CGAN, ACGAN, and D2GAN models and can learn a variety of valid information of the tested chip. It can distinguish between side channel data with and without hardware Trojan and classify hardware Trojan using the extended data. Furthermore, to compare the performance of the proposed model, we use the existing CGAN and ACGAN models equally for side channel information expansion and hardware Trojan detection. Finally, the designed hardware Trojan is implanted in an\u00a0encryption chip for generating data quality evaluation experiments and model method performance experiments. The results show that the average detection accuracy of the D2ACGAN-based hardware Trojan classification model can reach 97.08%, which is better than the detection models based on CNN, SVM, etc. The D2ACGAN model also outperforms the CGAN and ACGAN models in terms of generated data and hardware Trojan classification.\n<\/jats:p>","DOI":"10.1007\/s10836-023-06054-x","type":"journal-article","created":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T01:02:01Z","timestamp":1682730121000},"page":"129-140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Hardware Trojan Detection Method Based on Dual Discriminator Assisted Conditional Generation Adversarial Network"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9789-0908","authenticated-orcid":false,"given":"Wenjing","family":"Tang","sequence":"first","affiliation":[]},{"given":"Jing","family":"Su","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1504-8416","authenticated-orcid":false,"given":"Yuchan","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,29]]},"reference":[{"key":"6054_CR1","unstructured":"Yeh A (2012) Trends in the global IC design service market. DIGITIMES Res"},{"key":"6054_CR2","doi-asserted-by":"publisher","unstructured":"Bhunia S, Tehranipoor MM (2017)\u00a0The Hardware Trojan War: Attacks, Myths, and Defenses. 1st ed. Springer, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-319-68511-3","DOI":"10.1007\/978-3-319-68511-3"},{"key":"6054_CR3","doi-asserted-by":"publisher","unstructured":"Hayashi Y, Kawamura S (2020) Survey of hardware trojan threats and detection. In: International Symposium on Electromagnetic Compatibility-EMC EUROPE, pp. 1\u20135. Rome. https:\/\/doi.org\/10.1109\/EMCEUROPE48519.2020.9245675","DOI":"10.1109\/EMCEUROPE48519.2020.9245675"},{"key":"6054_CR4","doi-asserted-by":"publisher","unstructured":"Khamitkar R, Dube RR (2022)\u00a0A Survey on Using Machine Learning to Counter Hardware Trojan Challenges. In: ICT with Intelligent Applications, pp.539-547. Singapore. https:\/\/doi.org\/10.1007\/978-981-16-4177-0_53","DOI":"10.1007\/978-981-16-4177-0_53"},{"key":"6054_CR5","doi-asserted-by":"publisher","unstructured":"Jain A, Zhou Z, Guin U (2021)\u00a0Survey of Recent Developments for Hardware Trojan Detection. In: IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-5. Daegu. https:\/\/doi.org\/10.1109\/ISCAS51556.2021.9401143","DOI":"10.1109\/ISCAS51556.2021.9401143"},{"key":"6054_CR6","doi-asserted-by":"publisher","unstructured":"Wang X, Narasimhan S, Krishna A, Bhunia S (2012)\u00a0Side-channel analysis based reverse engineering for post-silicon validation. In: 25th International Conference on VLSI Design, pp. 304-309. IEEE, Hyderabad. https:\/\/doi.org\/10.1109\/VLSID.2012.88","DOI":"10.1109\/VLSID.2012.88"},{"key":"6054_CR7","doi-asserted-by":"publisher","unstructured":"Zhou Z, Guin U, Agrawal VD (2018)\u00a0Modeling and test generation for combinational hardware Trojans. In: 36th VLSI Test Symposium, pp. 1\u20136. IEEE, San Francisco. https:\/\/doi.org\/10.1109\/VTS.2018.8368626","DOI":"10.1109\/VTS.2018.8368626"},{"key":"6054_CR8","doi-asserted-by":"publisher","unstructured":"Farahmandi F, Huang Y, Mishra P (2017)\u00a0Trojan localization using symbolic algebra. In: 22nd Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 591-597. Chiba. https:\/\/doi.org\/10.1109\/ASPDAC.2017.7858388","DOI":"10.1109\/ASPDAC.2017.7858388"},{"key":"6054_CR9","doi-asserted-by":"publisher","unstructured":"Rad R, Plusquellic J, Tehranipoor M (2009)\u00a0A sensitivity analysis of power signal methods for detecting hardware Trojans under real process and environmental conditions. IEEE Trans Very Large Scale Integr (VLSI) Sys 18(12):1735-1744. https:\/\/doi.org\/10.1109\/TVLSI.2009.2029117","DOI":"10.1109\/TVLSI.2009.2029117"},{"key":"6054_CR10","doi-asserted-by":"publisher","unstructured":"Hossain FS, Shintani M, Inoue M, Orailoglu A (2018)\u00a0Variation-aware hardware Trojan detection through power side-channel. In: IEEE International Test Conference, pp. 1\u201310.IEEE, Phoenix. https:\/\/doi.org\/10.1109\/TEST.2018.8624866","DOI":"10.1109\/TEST.2018.8624866"},{"key":"6054_CR11","doi-asserted-by":"publisher","unstructured":"Nejat A, Hely D, Beroulle V (2015)\u00a0Facilitating side channel analysis by obfuscation for Hardware Trojan detection. In 2015 10th International Design & Test Symposium (IDT), pp.129-134. IEEE, Amman. https:\/\/doi.org\/10.1109\/IDT.2015.7396749","DOI":"10.1109\/IDT.2015.7396749"},{"key":"6054_CR12","doi-asserted-by":"publisher","first-page":"5124","DOI":"10.1109\/ACCESS.2018.2887268","volume":"7","author":"M Xue","year":"2018","unstructured":"Xue M, Bian R, Liu W (2018) Defeating Untrustworthy Testing Parties: A Novel Hybrid Clustering Ensemble Based Golden Models-Free Hardware Trojan Detection Method. IEEE Access 7:5124\u20135140. https:\/\/doi.org\/10.1109\/ACCESS.2018.2887268","journal-title":"IEEE Access"},{"issue":"2","key":"6054_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1587\/elex.16.20181065","volume":"16","author":"J He","year":"2019","unstructured":"He J, Liu Y, Yuan Y, Hu K, Xia X, Zhao Y (2019) Golden Chip Free Trojan Detection Leveraging Electromagnetic Side Channel Fingerprinting. IEICE Electronics Express 16(2):1\u20138. https:\/\/doi.org\/10.1587\/elex.16.20181065","journal-title":"IEICE Electronics Express"},{"key":"6054_CR14","doi-asserted-by":"publisher","unstructured":"Reshma K, Priyatharishini M, Nirmala Devi M (2019)\u00a0Hardware trojan detection using deep learning technique. In: Soft Computing and Signal Processing, pp. 671-680. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-13-3393-4_68","DOI":"10.1007\/978-981-13-3393-4_68"},{"key":"6054_CR15","doi-asserted-by":"publisher","unstructured":"Lu R, Shen H, Su Y, Li H, Li X (2019)\u00a0Gramsdet: Hardware trojan detection based on recurrent neural network. In: 28th Asian Test Symposium (ATS), pp. 111-1115. IEEE, Kolkata. https:\/\/doi.org\/10.1109\/ATS47505.2019.00021","DOI":"10.1109\/ATS47505.2019.00021"},{"key":"6054_CR16","doi-asserted-by":"publisher","unstructured":"Pu S, Yu Y, Wang W, Guo Z, Liu J, Gu D, Wang L, Gan J (2017)\u00a0Trace augmentation: What can be done even before preprocessing in a profiled sca? In: International Conference on Smart Card Research and Advanced Applications, pp. 232-247. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-75208-2_14","DOI":"10.1007\/978-3-319-75208-2_14"},{"key":"6054_CR17","doi-asserted-by":"publisher","unstructured":"Picek S, Heuser A, Jovic A, Bhasin S, Regazzoni F (2019)\u00a0The curse of class imbalance and conflicting metrics with machine learning for side-channel evaluations. IACR Trans Cryptographic Hardware Embedded Sys (1):1-29. https:\/\/doi.org\/10.13154\/tches.v2019.i1.209-237","DOI":"10.13154\/tches.v2019.i1.209-237"},{"key":"6054_CR18","unstructured":"Generative Adversarial Networks. https:\/\/arxiv.org\/abs\/1406.2661"},{"issue":"1","key":"6054_CR19","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks: An overview. IEEE Signal Processing Magazine 35(1):53\u201365. https:\/\/doi.org\/10.1109\/MSP.2017.2765202","journal-title":"IEEE Signal Processing Magazine"},{"key":"6054_CR20","unstructured":"Conditional Generative Adversarial Nets. https:\/\/arxiv.org\/abs\/1411.1784"},{"key":"6054_CR21","unstructured":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.\u00a0https:\/\/arxiv.org\/abs\/1511.06434"},{"key":"6054_CR22","unstructured":"Conditional Image Synthesis With Auxiliary Classifier GANs.\u00a0https:\/\/arxiv.org\/abs\/1610.09585"},{"key":"6054_CR23","unstructured":"Wasserstein GAN.\u00a0https:\/\/arxiv.org\/abs\/1701.07875v2"},{"issue":"5","key":"6054_CR24","doi-asserted-by":"publisher","first-page":"1594","DOI":"10.1080\/00207543.2019.1662133","volume":"58","author":"A Kusiak","year":"2020","unstructured":"Kusiak A (2020) Convolutional and generative adversarial neural networks in manufacturing. International Journal of Production Research 58(5):1594\u20131604. https:\/\/doi.org\/10.1080\/00207543.2019.1662133","journal-title":"International Journal of Production Research"},{"key":"6054_CR25","unstructured":"Data Synthesis based on Generative Adversarial Networks.\u00a0https:\/\/arxiv.org\/abs\/1806.03384v2"},{"key":"6054_CR26","doi-asserted-by":"publisher","first-page":"36322","DOI":"10.1109\/ACCESS.2019.2905015","volume":"7","author":"Z Pan","year":"2019","unstructured":"Pan Z, Yu W, Yi X, Khan A, Yuan F, Zheng Y (2019) Recent progress on generative adversarial networks (GANs): A survey. IEEE Access 7:36322\u201336333. https:\/\/doi.org\/10.1109\/ACCESS.2019.2905015","journal-title":"IEEE Access"},{"key":"6054_CR27","doi-asserted-by":"publisher","first-page":"35592","DOI":"10.1109\/ACCESS.2020.2974712","volume":"8","author":"AM Shaker","year":"2020","unstructured":"Shaker AM, Tantawi M, Shedeed HA, Tolba MF (2020) Generalization of convolutional neural networks for ECG classification using generative adversarial networks. IEEE Access 8:35592\u201335605. https:\/\/doi.org\/10.1109\/ACCESS.2020.2974712","journal-title":"IEEE Access"},{"key":"6054_CR28","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.eswa.2017.09.030","volume":"91","author":"G Douzas","year":"2018","unstructured":"Douzas G, Bacao F (2018) Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Systems with applications 91:464\u2013471. https:\/\/doi.org\/10.1016\/j.eswa.2017.09.030","journal-title":"Expert Systems with applications"},{"key":"6054_CR29","doi-asserted-by":"publisher","unstructured":"Kamal S, Mujeeb A, Supriya MH (2022)\u00a0Generative adversarial learning for improved data efficiency in underwater target classification. Eng Sci Technol Int J 30:101043. https:\/\/doi.org\/10.1016\/j.jestch.2021.07.006","DOI":"10.1016\/j.jestch.2021.07.006"},{"key":"6054_CR30","doi-asserted-by":"publisher","unstructured":"Dong F, Zhang Y, Nie X (2020)\u00a0Dual discriminator generative adversarial network for video anomaly detection. IEEE Access 8:88170\u201388176. https:\/\/doi.org\/10.1109\/ACCESS.2020.2993373","DOI":"10.1109\/ACCESS.2020.2993373"},{"key":"6054_CR31","unstructured":"Improved Techniques for Training GANs. https:\/\/arxiv.org\/abs\/\/1606.03498"},{"key":"6054_CR32","unstructured":"Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017)\u00a0Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv Neural Inform Proc Sys 30"},{"key":"6054_CR33","unstructured":"Computational Optimal Transport. https:\/\/arxiv.org\/abs\/1803.00567"},{"key":"6054_CR34","doi-asserted-by":"publisher","unstructured":"Madden K, Harkin J, McDaid L, Nugent C (2018)\u00a0Adding Security to Networks-on-Chip using Neural Networks. In: Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1299\u20131306. Bangalore, India.\u00a0https:\/\/doi.org\/10.1109\/ssci.2018.8628832","DOI":"10.1109\/ssci.2018.8628832"},{"key":"6054_CR35","doi-asserted-by":"publisher","unstructured":"Reshma K, Priyatharishini M, Nirmala Devi M (2019)\u00a0Hardware Trojan Detection Using Deep Learning Technique. In: Soft Computing and Signal Processing; Advances in Intelligent Systems and Computing, pp. 671\u2013680. Springer: Singapore.\u00a0https:\/\/doi.org\/10.1007\/978-981-13-3393-4_68","DOI":"10.1007\/978-981-13-3393-4_68"},{"key":"6054_CR36","doi-asserted-by":"publisher","unstructured":"Hu T, Dian S, Jiang R (2020) Hardware Trojan detection based on long short-term memory neural network. Eng 46:110\u2013115. https:\/\/doi.org\/10.19678\/j.issn.1000-3428.0055589","DOI":"10.19678\/j.issn.1000-3428.0055589"}],"container-title":["Journal of Electronic Testing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10836-023-06054-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10836-023-06054-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10836-023-06054-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T06:27:34Z","timestamp":1687847254000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10836-023-06054-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4]]},"references-count":36,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["6054"],"URL":"https:\/\/doi.org\/10.1007\/s10836-023-06054-x","relation":{},"ISSN":["0923-8174","1573-0727"],"issn-type":[{"type":"print","value":"0923-8174"},{"type":"electronic","value":"1573-0727"}],"subject":[],"published":{"date-parts":[[2023,4]]},"assertion":[{"value":"25 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}