{"id":"https://openalex.org/W4414605336","doi":"https://doi.org/10.1109/qrs65678.2025.00042","title":"Adversarial Generation of Deep Neural Network Image Test Cases Based on Multi-Conditional Constraints","display_name":"Adversarial Generation of Deep Neural Network Image Test Cases Based on Multi-Conditional Constraints","publication_year":2025,"publication_date":"2025-07-16","ids":{"openalex":"https://openalex.org/W4414605336","doi":"https://doi.org/10.1109/qrs65678.2025.00042"},"language":"en","primary_location":{"id":"doi:10.1109/qrs65678.2025.00042","is_oa":false,"landing_page_url":"https://doi.org/10.1109/qrs65678.2025.00042","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 25th International Conference on Software Quality, Reliability and Security (QRS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079059647","display_name":"Qingxia Yu","orcid":"https://orcid.org/0009-0002-7296-7205"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingxia Yu","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics,Nanjing,China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043186866","display_name":"Chuanqi Tao","orcid":null},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuanqi Tao","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics,Nanjing,China","institution_ids":["https://openalex.org/I9842412"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I9842412"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"337","last_page":"346"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9805999994277954,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9707000255584717,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5504000186920166},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5005000233650208},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.4984999895095825},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45590001344680786},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.4120999872684479},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.397599995136261},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.3725000023841858},{"id":"https://openalex.org/keywords/test-case","display_name":"Test case","score":0.362199991941452},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.35690000653266907}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5932000279426575},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5541999936103821},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5504000186920166},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5005000233650208},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.4984999895095825},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45590001344680786},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.4120999872684479},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4117000102996826},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.397599995136261},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3725000023841858},{"id":"https://openalex.org/C128942645","wikidata":"https://www.wikidata.org/wiki/Q1568346","display_name":"Test case","level":3,"score":0.362199991941452},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.35690000653266907},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.33709999918937683},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33660000562667847},{"id":"https://openalex.org/C126953365","wikidata":"https://www.wikidata.org/wiki/Q5438152","display_name":"Fault coverage","level":3,"score":0.3240000009536743},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C180462255","wikidata":"https://www.wikidata.org/wiki/Q3559736","display_name":"Standard test image","level":4,"score":0.28380000591278076},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2567000091075897},{"id":"https://openalex.org/C2779696439","wikidata":"https://www.wikidata.org/wiki/Q7512811","display_name":"Signature (topology)","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/qrs65678.2025.00042","is_oa":false,"landing_page_url":"https://doi.org/10.1109/qrs65678.2025.00042","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 25th International Conference on Software Quality, Reliability and Security (QRS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1738075544","display_name":null,"funder_award_id":"62402215","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6922403245","display_name":null,"funder_award_id":"NT2024020","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2475287302","https://openalex.org/W2765811365","https://openalex.org/W2804337238","https://openalex.org/W2954629067","https://openalex.org/W2954678575","https://openalex.org/W2957905354","https://openalex.org/W2962793481","https://openalex.org/W2963073614","https://openalex.org/W2963709863","https://openalex.org/W2982138961","https://openalex.org/W3042703469","https://openalex.org/W3103014337","https://openalex.org/W3103123363","https://openalex.org/W3173411326","https://openalex.org/W4220858971","https://openalex.org/W4284702108","https://openalex.org/W4319663674","https://openalex.org/W4389077265"],"related_works":[],"abstract_inverted_index":{"The":[0],"wide":[1],"range":[2],"of":[3,5,40,66,105,168,199,255],"applications":[4],"deep":[6],"neural":[7],"networks":[8],"in":[9,197],"image":[10,107,123,178],"recognition,":[11],"medical":[12],"diagnosis,":[13],"and":[14,37,58,68,97,109,161,186],"safetycritical":[15],"areas":[16],"makes":[17],"it":[18],"essential":[19],"to":[20,32,82,110,121,204,217,264,272],"test":[21,84,99,124,141,206,235,269],"them":[22],"adequately.":[23],"However,":[24],"traditional":[25],"software":[26],"testing":[27,43],"methodologies":[28],"proves":[29],"challenging":[30],"due":[31],"the":[33,64,71,77,103,114,147,166,187,211,221,225,233,242,253,256,260,267],"inherent":[34],"complex":[35],"topology":[36],"blackbox":[38],"characteristics":[39],"DNNs.":[41],"Existing":[42],"techniques":[44],"(e.g.,":[45],"coverage-guided":[46],"testing,":[47,49],"fuzzy":[48],"etc.)":[50],"generally":[51,209],"lack":[52,65],"guidance":[53,67],"mechanisms":[54],"for":[55,248],"generating":[56,106],"samples":[57,101],"similar":[59,96],"fault":[60,88,129,200,212],"characteristics.":[61],"To":[62,164],"address":[63],"constraints":[69,223],"on":[70,146,174,224,266],"generated":[72,95,234,244],"samples,":[73],"a":[74,138],"conditional":[75],"metric,":[76],"Gini":[78,162],"impurity,":[79],"is":[80,119,194],"introduced":[81,120],"generate":[83,122],"inputs":[85],"with":[86,126,180,220,237],"high":[87],"detection":[89,201,213],"rates.":[90],"In":[91,132],"addition,":[92],"many":[93],"randomly":[94],"redundant":[98],"case":[100,142],"ignore":[102],"problem":[104],"diversity,":[108],"solve":[111],"this":[112,133,246],"problem,":[113],"geometric":[115,157,226],"diversity":[116,158,227],"(GD)":[117,159],"score":[118],"cases":[125,236],"more":[127,195],"different":[128],"feature":[130],"types.":[131],"paper,":[134],"we":[135,171,229],"propose":[136],"DeepMCC,":[137],"multi-conditional":[139],"constraintbased":[140],"generation":[143],"method":[144,193,247],"based":[145],"Conditional":[148],"Generative":[149],"Adversarial":[150],"Networks":[151],"(CGANs)":[152],"framework,":[153],"which":[154,208,258],"innovatively":[155],"introduces":[156],"scores":[160],"impurity.":[163],"validate":[165],"effectiveness":[167],"our":[169,192],"method,":[170],"conduct":[172],"experiments":[173],"four":[175],"publicly":[176],"available":[177],"datasets":[179],"five":[181],"widely":[182],"used":[183],"DNN":[184],"models,":[185],"results":[188],"show":[189],"that":[190],"(1)":[191],"effective":[196],"terms":[198],"rate":[202,214],"compared":[203,271],"black-box":[205],"methods,":[207],"improves":[210,259],"by":[215,245,262],"1%":[216],"2%;":[218],"(2)":[219],"preset":[222],"scores,":[228],"can":[230,250],"precisely":[231],"control":[232],"higher":[238],"diversity;":[239],"(3)":[240],"using":[241],"images":[243],"retraining":[249],"effectively":[251],"improve":[252],"accuracy":[254,261],"model,":[257],"0.1%":[263],"0.4%":[265],"original":[268],"set":[270],"other":[273],"methods.":[274]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}