{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T06:05:05Z","timestamp":1784181905675,"version":"3.55.0"},"reference-count":114,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172202"],"award-info":[{"award-number":["62172202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272221"],"award-info":[{"award-number":["62272221"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62572226"],"award-info":[{"award-number":["62572226"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Science of Computer Programming"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.scico.2026.103476","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T18:02:26Z","timestamp":1774461746000},"page":"103476","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["MutDBD: Mutation-based training set diagnosis for backdoor defense in deep neural networks"],"prefix":"10.1016","volume":"253","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4800-6255","authenticated-orcid":false,"given":"Mingliang","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2282-7175","authenticated-orcid":false,"given":"Yanhui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2352-2226","authenticated-orcid":false,"given":"Lin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuming","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"7553","key":"10.1016\/j.scico.2026.103476_bib0001","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.scico.2026.103476_bib0002","series-title":"AAAI","first-page":"19823","article-title":"Conditional backdoor attack via JPEG compression","author":"Duan","year":"2024"},{"key":"10.1016\/j.scico.2026.103476_bib0003","series-title":"AAAI","first-page":"21072","article-title":"Invisible backdoor attack against 3D point cloud classifier in graph spectral domain","author":"Fan","year":"2024"},{"key":"10.1016\/j.scico.2026.103476_bib0004","series-title":"2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10\u201317, 2021","first-page":"16443","article-title":"Invisible backdoor attack with sample-specific triggers","author":"Li","year":"2021"},{"key":"10.1016\/j.scico.2026.103476_bib0005","series-title":"CVPR Workshops","first-page":"3439","article-title":"Look, listen, and attack: backdoor attacks against video action recognition","author":"Hammoud","year":"2024"},{"key":"10.1016\/j.scico.2026.103476_bib0006","series-title":"AAAI","first-page":"9698","article-title":"Backdoor attacks against no-reference image quality assessment models via a scalable trigger","author":"Yu","year":"2025"},{"key":"10.1016\/j.scico.2026.103476_bib0007","series-title":"SP","first-page":"2067","article-title":"DeepVenom: persistent DNN backdoors exploiting transient weight perturbations in memories","author":"Cai","year":"2024"},{"key":"10.1016\/j.scico.2026.103476_bib0008","doi-asserted-by":"crossref","first-page":"5852","DOI":"10.1109\/TIFS.2024.3404885","article-title":"Toward stealthy backdoor attacks against speech recognition via elements of sound","volume":"19","author":"Cai","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"3","key":"10.1016\/j.scico.2026.103476_bib0009","doi-asserted-by":"crossref","first-page":"78:1","DOI":"10.1145\/3701985","article-title":"Backdoor attacks against voice recognition systems: a survey","volume":"57","author":"Yan","year":"2025","journal-title":"ACM Comput. Surv."},{"issue":"4","key":"10.1016\/j.scico.2026.103476_bib0010","doi-asserted-by":"crossref","first-page":"92:1","DOI":"10.1145\/3640333","article-title":"Exploring semantic redundancy using backdoor triggers: a complementary insight into the challenges facing DNN-based software vulnerability detection","volume":"33","author":"Shao","year":"2024","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"10.1016\/j.scico.2026.103476_bib0011","series-title":"CVPR","first-page":"14885","article-title":"Adversarial backdoor attack by naturalistic data poisoning on trajectory prediction in autonomous driving","author":"Pourkeshavarz","year":"2024"},{"issue":"8","key":"10.1016\/j.scico.2026.103476_bib0012","doi-asserted-by":"crossref","first-page":"2617","DOI":"10.1109\/JSAC.2021.3087237","article-title":"Defense-resistant backdoor attacks against deep neural networks in outsourced cloud environment","volume":"39","author":"Gong","year":"2021","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"10.1016\/j.scico.2026.103476_bib0013","series-title":"Blockchain-empowered internet of things (IoTs) platforms for automation in various sectors","first-page":"443","author":"Addula","year":"2024"},{"issue":"1","key":"10.1016\/j.scico.2026.103476_bib0014","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/TPAMI.2025.3611340","article-title":"Defenses in adversarial machine learning: a systematic survey from the lifecycle perspective","volume":"48","author":"Wu","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.scico.2026.103476_bib0015","series-title":"NeurIPS","first-page":"14900","article-title":"Anti-backdoor learning: training clean models on poisoned data","author":"Li","year":"2021"},{"key":"10.1016\/j.scico.2026.103476_bib0016","series-title":"ICLR","article-title":"Backdoor defense via decoupling the training process","author":"Huang","year":"2022"},{"key":"10.1016\/j.scico.2026.103476_bib0017","series-title":"CVPR","first-page":"4005","article-title":"Backdoor defense via adaptively splitting poisoned dataset","author":"Gao","year":"2023"},{"issue":"5","key":"10.1016\/j.scico.2026.103476_bib0018","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1109\/TSE.2020.3034721","article-title":"Towards security threats of deep learning systems: a survey","volume":"48","author":"He","year":"2022","journal-title":"IEEE Trans. Software Eng."},{"key":"10.1016\/j.scico.2026.103476_bib0019","series-title":"ICSE","first-page":"739","article-title":"Towards characterizing adversarial defects of deep learning software from the lens of uncertainty","author":"Zhang","year":"2020"},{"key":"10.1016\/j.scico.2026.103476_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.jss.2023.111859","article-title":"Detection of backdoor attacks using targeted universal adversarial perturbations for deep neural networks","volume":"207","author":"Qu","year":"2024","journal-title":"J. Syst. Softw."},{"key":"10.1016\/j.scico.2026.103476_bib0021","series-title":"ICSE","first-page":"359","article-title":"Misbehaviour prediction for autonomous driving systems","author":"Stocco","year":"2020"},{"issue":"3","key":"10.1016\/j.scico.2026.103476_bib0022","doi-asserted-by":"crossref","first-page":"68:1","DOI":"10.1145\/3631977","article-title":"Attack as detection: using adversarial attack methods to detect abnormal examples","volume":"33","author":"Zhao","year":"2024","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"10.1016\/j.scico.2026.103476_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.infsof.2020.106413","article-title":"Boundary sampling to boost mutation testing for deep learning models","volume":"130","author":"Shen","year":"2021","journal-title":"Inf. Softw. Technol."},{"key":"10.1016\/j.scico.2026.103476_bib0024","doi-asserted-by":"crossref","first-page":"4285","DOI":"10.1109\/TIFS.2024.3376968","article-title":"BDMMT: backdoor sample detection for language models through model mutation testing","volume":"19","author":"Wei","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.scico.2026.103476_bib0025","series-title":"ICSE","first-page":"1245","article-title":"Adversarial sample detection for deep neural network through model mutation testing","author":"Wang","year":"2019"},{"key":"10.1016\/j.scico.2026.103476_bib0026","unstructured":"A. Krizhevsky, G. Hinton, et al., Learning multiple layers of features from tiny images (2009)."},{"key":"10.1016\/j.scico.2026.103476_bib0027","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.neunet.2012.02.016","article-title":"Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition","volume":"32","author":"Stallkamp","year":"2012","journal-title":"Neural Netw."},{"key":"10.1016\/j.scico.2026.103476_bib0028","series-title":"CVPR","first-page":"248","article-title":"ImageNet: a large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.scico.2026.103476_bib0029","series-title":"CVPR","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.scico.2026.103476_bib0030","series-title":"ICLR","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2015"},{"key":"10.1016\/j.scico.2026.103476_bib0031","series-title":"CVPR","first-page":"2261","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.scico.2026.103476_bib0032","doi-asserted-by":"crossref","first-page":"47230","DOI":"10.1109\/ACCESS.2019.2909068","article-title":"BadNets: evaluating backdooring attacks on deep neural networks","volume":"7","author":"Gu","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.scico.2026.103476_bib0033","series-title":"NeurIPS","article-title":"Input-aware dynamic backdoor attack","author":"Nguyen","year":"2020"},{"key":"10.1016\/j.scico.2026.103476_bib0034","series-title":"ECCV (10)","first-page":"182","article-title":"Reflection backdoor: a natural backdoor attack on deep neural networks","volume":"12355","author":"Liu","year":"2020"},{"key":"10.1016\/j.scico.2026.103476_bib0035","series-title":"ICLR","article-title":"WaNet - imperceptible warping-based backdoor attack","author":"Nguyen","year":"2021"},{"key":"10.1016\/j.scico.2026.103476_bib0036","series-title":"NeurIPS","article-title":"Effective backdoor defense by exploiting sensitivity of poisoned samples","author":"Chen","year":"2022"},{"key":"10.1016\/j.scico.2026.103476_bib0037","doi-asserted-by":"crossref","first-page":"6984","DOI":"10.1109\/TIFS.2025.3586499","article-title":"Unified neural backdoor removal with only few clean samples through unlearning and relearning","volume":"20","author":"Min","year":"2025","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"4","key":"10.1016\/j.scico.2026.103476_bib0038","first-page":"116:1","article-title":"Grammar mutation for testing input parsers","volume":"34","author":"Bendrissou","year":"2025","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"10.1016\/j.scico.2026.103476_bib0039","series-title":"ISSTA","first-page":"449","article-title":"PIT: a practical mutation testing tool for java (demo)","author":"Coles","year":"2016"},{"key":"10.1016\/j.scico.2026.103476_bib0040","series-title":"ICSE (Companion Volume)","first-page":"33","article-title":"MDroid+: a mutation testing framework for android","author":"Moran","year":"2018"},{"key":"10.1016\/j.scico.2026.103476_bib0041","series-title":"ASE","first-page":"1198","article-title":"MuSC: a tool for mutation testing of ethereum smart contract","author":"Li","year":"2019"},{"issue":"11","key":"10.1016\/j.scico.2026.103476_bib0042","doi-asserted-by":"crossref","DOI":"10.1002\/smr.2450","article-title":"Towards practical application of mutation testing in industry - Traditional versus extreme mutation testing","volume":"34","author":"Betka","year":"2022","journal-title":"J. Softw. Evol. Process."},{"key":"10.1016\/j.scico.2026.103476_bib0043","series-title":"ESEC\/SIGSOFT FSE","first-page":"250","article-title":"Contextual predictive mutation testing","author":"Jain","year":"2023"},{"key":"10.1016\/j.scico.2026.103476_bib0044","series-title":"ICSE","first-page":"1743","article-title":"Prioritizing mutants to guide mutation testing","author":"Kaufman","year":"2022"},{"issue":"8","key":"10.1016\/j.scico.2026.103476_bib0045","article-title":"HOTFUZ: cost-effective higher-order mutation-based fault localization","volume":"32","author":"Jang","year":"2022","journal-title":"Softw. Test. Verification Reliab."},{"issue":"6","key":"10.1016\/j.scico.2026.103476_bib0046","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1109\/TSE.2021.3052987","article-title":"Enhancement of mutation testing via fuzzy clustering and multi-population genetic algorithm","volume":"48","author":"Dang","year":"2022","journal-title":"IEEE Trans. Software Eng."},{"key":"10.1016\/j.scico.2026.103476_bib0047","series-title":"USENIX Security Symposium","first-page":"4535","article-title":"Systematic assessment of fuzzers using mutation analysis","author":"G\u00f6rz","year":"2023"},{"key":"10.1016\/j.scico.2026.103476_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.jss.2024.112281","article-title":"Integrating neural mutation into mutation-based fault localization: a hybrid approach","volume":"221","author":"Liu","year":"2025","journal-title":"J. Syst. Softw."},{"key":"10.1016\/j.scico.2026.103476_bib0049","series-title":"CAiSE","first-page":"482","article-title":"Mutation operators for large scale data processing programs in spark","volume":"12127","author":"de Souza Neto","year":"2020"},{"issue":"2","key":"10.1016\/j.scico.2026.103476_bib0050","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TSE.2020.2982638","article-title":"Enabling mutant generation for open- and closed-source android apps","volume":"48","author":"Escobar-Vel\u00e1squez","year":"2022","journal-title":"IEEE Trans. Software Eng."},{"key":"10.1016\/j.scico.2026.103476_bib0051","series-title":"ISSTA","first-page":"797","article-title":"QMutPy: a mutation testing tool for quantum algorithms and applications in qiskit","author":"Fortunato","year":"2022"},{"key":"10.1016\/j.scico.2026.103476_bib0052","series-title":"ISSTA","first-page":"388","article-title":"Interval constraint-based mutation testing of numerical specifications","author":"Jeangoudoux","year":"2021"},{"key":"10.1016\/j.scico.2026.103476_bib0053","series-title":"AST@ICSE","first-page":"31","article-title":"Validating test case migration via mutation analysis","author":"Jovanovikj","year":"2020"},{"key":"10.1016\/j.scico.2026.103476_bib0054","series-title":"ESEC\/SIGSOFT FSE","first-page":"1721","article-title":"JSIMutate: Understanding performance results through mutations","author":"Laurent","year":"2022"},{"issue":"5","key":"10.1016\/j.scico.2026.103476_bib0055","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1007\/s10664-021-10001-9","article-title":"A requirements inspection method based on scenarios generated by model mutation and the experimental validation","volume":"26","author":"Li","year":"2021","journal-title":"Empir. Softw. Eng."},{"key":"10.1016\/j.scico.2026.103476_bib0056","series-title":"ISSTA","first-page":"263","article-title":"On the use of mutation analysis for evaluating student test suite quality","author":"Perretta","year":"2022"},{"issue":"1","key":"10.1016\/j.scico.2026.103476_bib0057","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10664-023-10385-w","article-title":"Mutation analysis for evaluating code translation","volume":"29","author":"Guizzo","year":"2024","journal-title":"Empir. Softw. Eng."},{"key":"10.1016\/j.scico.2026.103476_bib0058","series-title":"ISSTA","first-page":"1912","article-title":"Integrating mutation techniques to keep specification and source code in sync","author":"Jacob","year":"2024"},{"key":"10.1016\/j.scico.2026.103476_bib0059","series-title":"MODELS","first-page":"228","article-title":"Mutation testing for temporal alloy models","author":"Jovanovic","year":"2023"},{"key":"10.1016\/j.scico.2026.103476_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.parco.2024.103097","article-title":"An automated OpenMP mutation testing framework for performance optimization","volume":"121","author":"Miao","year":"2024","journal-title":"Parallel Comput."},{"key":"10.1016\/j.scico.2026.103476_bib0061","series-title":"ESEC\/SIGSOFT FSE","first-page":"262","article-title":"\u03bcAkka: mutation testing for actor concurrency in akka using real-world bugs","author":"Moradi-Moghadam","year":"2023"},{"issue":"2","key":"10.1016\/j.scico.2026.103476_bib0062","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TSE.2019.2962027","article-title":"Machine learning testing: survey, landscapes and horizons","volume":"48","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Software Eng."},{"key":"10.1016\/j.scico.2026.103476_bib0063","series-title":"ICSE","first-page":"397","article-title":"Prioritizing test inputs for deep neural networks via mutation analysis","author":"Wang","year":"2021"},{"key":"10.1016\/j.scico.2026.103476_bib0064","series-title":"ASE","first-page":"355","article-title":"Deepmetis: augmenting a deep learning test set to increase its mutation score","author":"Riccio","year":"2021"},{"issue":"1","key":"10.1016\/j.scico.2026.103476_bib0065","doi-asserted-by":"crossref","first-page":"22:1","DOI":"10.1145\/3607191","article-title":"GraphPrior: mutation-based test input prioritization for graph neural networks","volume":"33","author":"Dang","year":"2024","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"10.1016\/j.scico.2026.103476_bib0066","series-title":"Ase","first-page":"1301","article-title":"Mutation-based fault localization of deep neural networks","author":"Ghanbari","year":"2023"},{"key":"10.1016\/j.scico.2026.103476_bib0067","series-title":"ISSTA","first-page":"1669","article-title":"Decomposition of deep neural networks into modules via mutation analysis","author":"Ghanbari","year":"2024"},{"key":"10.1016\/j.scico.2026.103476_bib0068","series-title":"ICSE Companion","first-page":"68","article-title":"DeepCrime: from real faults to mutation testing tool for deep learning","author":"Humbatova","year":"2023"},{"key":"10.1016\/j.scico.2026.103476_bib0069","series-title":"ICSE","first-page":"1418","article-title":"Muffin: testing deep learning libraries via neural architecture fuzzing","author":"Gu","year":"2022"},{"key":"10.1016\/j.scico.2026.103476_bib0070","series-title":"ICSME","first-page":"47","article-title":"The unit test quality of deep learning libraries: a mutation analysis","author":"Jia","year":"2021"},{"key":"10.1016\/j.scico.2026.103476_bib0071","series-title":"ESEC\/SIGSOFT FSE","first-page":"788","article-title":"Deep learning library testing via effective model generation","author":"Wang","year":"2020"},{"key":"10.1016\/j.scico.2026.103476_bib0072","series-title":"ASE","first-page":"1533","article-title":"DevMuT: testing deep learning framework via developer expertise-based mutation","author":"Mu","year":"2024"},{"key":"10.1016\/j.scico.2026.103476_bib0073","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.ins.2022.04.020","article-title":"Robustness evaluation for deep neural networks via mutation decision boundaries analysis","volume":"601","author":"Lin","year":"2022","journal-title":"Inf. Sci."},{"key":"10.1016\/j.scico.2026.103476_bib0074","series-title":"ASE","first-page":"1708","article-title":"MUTEN: mutant-based ensembles for boosting gradient-based adversarial attack","author":"Hu","year":"2023"},{"key":"10.1016\/j.scico.2026.103476_bib0075","series-title":"ESEC\/SIGSOFT FSE","first-page":"994","article-title":"Fairea: a model behaviour mutation approach to benchmarking bias mitigation methods","author":"Hort","year":"2021"},{"key":"10.1016\/j.scico.2026.103476_bib0076","series-title":"ICSE Companion","first-page":"79","article-title":"MutaBot: a mutation testing approach for chatbots","author":"Urrico","year":"2024"},{"key":"10.1016\/j.scico.2026.103476_bib0077","series-title":"ESEM","first-page":"1","article-title":"How do deep learning faults affect AI-enabled cyber-physical systems in operation? a preliminary study based on deepcrime mutation operators","author":"Arrieta","year":"2023"},{"key":"10.1016\/j.scico.2026.103476_bib0078","doi-asserted-by":"crossref","DOI":"10.1016\/j.sysarc.2023.103050","article-title":"Mutation testing of unsupervised learning systems","volume":"146","author":"Lu","year":"2024","journal-title":"J. Syst. Archit."},{"key":"10.1016\/j.scico.2026.103476_bib0079","doi-asserted-by":"crossref","DOI":"10.1016\/j.sysarc.2022.102701","article-title":"Towards mutation testing of reinforcement learning systems","volume":"131","author":"Lu","year":"2022","journal-title":"J. Syst. Archit."},{"key":"10.1016\/j.scico.2026.103476_bib0080","series-title":"SafeAI@AAAI","article-title":"Detecting backdoor attacks on deep neural networks by activation clustering","volume":"2301","author":"Chen","year":"2019"},{"key":"10.1016\/j.scico.2026.103476_bib0081","series-title":"ICLR","article-title":"Activation gradient based poisoned sample detection against backdoor attacks","author":"Yuan","year":"2025"},{"key":"10.1016\/j.scico.2026.103476_bib0082","series-title":"ICML","article-title":"IBD-PSC: input-level backdoor detection via parameter-oriented scaling consistency","author":"Hou","year":"2024"},{"key":"10.1016\/j.scico.2026.103476_bib0083","series-title":"IJCAI","first-page":"4658","article-title":"Deepinspect: a black-box trojan detection and mitigation framework for deep neural networks","author":"Chen","year":"2019"},{"key":"10.1016\/j.scico.2026.103476_bib0084","unstructured":"Y. Li, J. He, H. Huang, J. Sun, X. Ma, Shortcuts everywhere and nowhere: exploring multi-trigger backdoor attacks, arXiv preprint arXiv: 2401.15295(2024)."},{"key":"10.1016\/j.scico.2026.103476_bib0085","series-title":"CVPR","first-page":"588","article-title":"Quarantine: sparsity can uncover the trojan attack trigger for free","author":"Chen","year":"2022"},{"key":"10.1016\/j.scico.2026.103476_bib0086","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1109\/TIP.2025.3539466","article-title":"Contrastive neuron pruning for backdoor defense","volume":"34","author":"Feng","year":"2025","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.scico.2026.103476_bib0087","series-title":"ECCV (62)","first-page":"262","article-title":"UNIT: backdoor mitigation via automated neural distribution tightening","volume":"15120","author":"Cheng","year":"2024"},{"key":"10.1016\/j.scico.2026.103476_bib0088","article-title":"Seal your backdoor with variational defense","volume":"abs\/2503.08829","author":"Sabolic","year":"2025","journal-title":"CoRR"},{"key":"10.1016\/j.scico.2026.103476_bib0089","series-title":"ICASSP","first-page":"3855","article-title":"Strong data augmentation sanitizes poisoning and backdoor attacks without an accuracy tradeoff","author":"Borgnia","year":"2021"},{"key":"10.1016\/j.scico.2026.103476_bib0090","series-title":"AAAI","first-page":"11425","article-title":"Progressive poisoned data isolation for training-Time backdoor defense","author":"Chen","year":"2024"},{"key":"10.1016\/j.scico.2026.103476_bib0091","series-title":"ICLR","article-title":"Protecting against simultaneous data poisoning attacks","author":"Alex","year":"2025"},{"key":"10.1016\/j.scico.2026.103476_bib0092","series-title":"ASE","first-page":"126","article-title":"Evolutionary robustness testing of data processing systems using models and data mutation (t)","author":"Nardo","year":"2015"},{"key":"10.1016\/j.scico.2026.103476_bib0093","series-title":"ISSRE","first-page":"100","article-title":"Deepmutation: mutation testing of deep learning systems","author":"Ma","year":"2018"},{"key":"10.1016\/j.scico.2026.103476_bib0094","series-title":"ICML","first-page":"23803","article-title":"Cross-entropy loss functions: theoretical analysis and applications","volume":"202","author":"Mao","year":"2023"},{"key":"10.1016\/j.scico.2026.103476_bib0095","series-title":"SP","first-page":"103","article-title":"Detecting AI trojans using meta neural analysis","author":"Xu","year":"2021"},{"key":"10.1016\/j.scico.2026.103476_bib0096","series-title":"CCS","first-page":"1265","article-title":"ABS: scanning neural networks for back-doors by artificial brain stimulation","author":"Liu","year":"2019"},{"key":"10.1016\/j.scico.2026.103476_bib0097","article-title":"BackdoorBox: a python toolbox for backdoor learning","volume":"abs\/2302.01762","author":"Li","year":"2023","journal-title":"CoRR"},{"key":"10.1016\/j.scico.2026.103476_bib0098","series-title":"IEEE Symposium on Security and Privacy","first-page":"707","article-title":"Neural cleanse: identifying and mitigating backdoor attacks in neural networks","author":"Wang","year":"2019"},{"key":"10.1016\/j.scico.2026.103476_bib0099","series-title":"ACSAC","first-page":"113","article-title":"STRIP: a defence against trojan attacks on deep neural networks","author":"Gao","year":"2019"},{"issue":"1","key":"10.1016\/j.scico.2026.103476_bib0100","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The pascal visual object classes challenge: a retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.scico.2026.103476_bib0101","series-title":"CVPR","first-page":"3313","article-title":"Fast and unsupervised action boundary detection for action segmentation","author":"Du","year":"2022"},{"issue":"6","key":"10.1016\/j.scico.2026.103476_bib0102","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","article-title":"Individual comparisons by ranking methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Biometrics Bulletin"},{"key":"10.1016\/j.scico.2026.103476_bib0103","series-title":"Annual Meeting of the Southern Association for Institutional Research","first-page":"1","article-title":"Exploring methods for evaluating group differences on the NSSE and other surveys: are the t-test and cohen\u00b4sd indices the most appropriate choices","author":"Romano","year":"2006"},{"key":"10.1016\/j.scico.2026.103476_bib0104","series-title":"ICSE","first-page":"623","article-title":"Deeptralog: trace-Log combined microservice anomaly detection through graph-based deep learning","author":"Zhang","year":"2022"},{"key":"10.1016\/j.scico.2026.103476_bib0105","series-title":"ASE","first-page":"1158","article-title":"Deepmutation++: a mutation testing framework for deep learning systems","author":"Hu","year":"2019"},{"issue":"2","key":"10.1016\/j.scico.2026.103476_bib0106","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1145\/3730577","article-title":"Less is more: feature engineering for fairness and performance of machine learning software","volume":"35","author":"Meng","year":"2026","journal-title":"ACM Trans. Software Eng. Method."},{"key":"10.1016\/j.scico.2026.103476_bib0107","series-title":"Noise Reduction in Speech Processing","first-page":"1","article-title":"Pearson correlation coefficient","author":"Benesty","year":"2009"},{"key":"10.1016\/j.scico.2026.103476_bib0108","series-title":"NeurIPS","article-title":"PKD: general distillation framework for object detectors via pearson correlation coefficient","author":"Cao","year":"2022"},{"issue":"4","key":"10.1016\/j.scico.2026.103476_bib0109","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3643678","article-title":"Test optimization in dnn testing: a survey","volume":"33","author":"Hu","year":"2024","journal-title":"ACM Trans. Software Eng. Method."},{"issue":"6","key":"10.1016\/j.scico.2026.103476_bib0110","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3644388","article-title":"Deepgd: a multi-objective black-box test selection approach for deep neural networks","volume":"33","author":"Aghababaeyan","year":"2024","journal-title":"ACM Trans. Software Eng. Method."},{"key":"10.1016\/j.scico.2026.103476_bib0111","series-title":"NeurIPS","first-page":"18021","article-title":"Manipulating SGD with data ordering attacks","author":"Shumailov","year":"2021"},{"key":"10.1016\/j.scico.2026.103476_bib0112","series-title":"2017 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD)","first-page":"131","article-title":"Fault injection attack on deep neural network","author":"Liu","year":"2017"},{"key":"10.1016\/j.scico.2026.103476_bib0113","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13347","article-title":"Towards practical deployment-stage backdoor attack on deep neural networks","author":"Qi","year":"2022"},{"issue":"11","key":"10.1016\/j.scico.2026.103476_bib0114","doi-asserted-by":"crossref","first-page":"7928","DOI":"10.1109\/TPAMI.2021.3112932","article-title":"T-bfa: targeted bit-flip adversarial weight attack","volume":"44","author":"Rakin","year":"2021","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["Science of Computer Programming"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167642326000420?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167642326000420?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:09:32Z","timestamp":1784178572000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167642326000420"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":114,"alternative-id":["S0167642326000420"],"URL":"https:\/\/doi.org\/10.1016\/j.scico.2026.103476","relation":{},"ISSN":["0167-6423"],"issn-type":[{"value":"0167-6423","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MutDBD: Mutation-based training set diagnosis for backdoor defense in deep neural networks","name":"articletitle","label":"Article Title"},{"value":"Science of Computer Programming","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.scico.2026.103476","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103476"}}