{"id":"https://openalex.org/W4411478996","doi":"https://doi.org/10.1007/s11063-025-11777-3","title":"Enhancing Deep Learning with Resilient Adversarial Network (RANet): An Advanced Adversarial Resilience Training Framework for Robust Image Classification","display_name":"Enhancing Deep Learning with Resilient Adversarial Network (RANet): An Advanced Adversarial Resilience Training Framework for Robust Image Classification","publication_year":2025,"publication_date":"2025-06-20","ids":{"openalex":"https://openalex.org/W4411478996","doi":"https://doi.org/10.1007/s11063-025-11777-3"},"language":"en","primary_location":{"id":"doi:10.1007/s11063-025-11777-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-025-11777-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-025-11777-3.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11063-025-11777-3.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053674667","display_name":"Saleh Alyahyan","orcid":"https://orcid.org/0000-0002-7740-9635"},"institutions":[{"id":"https://openalex.org/I206935292","display_name":"Shaqra University","ror":"https://ror.org/05hawb687","country_code":"SA","type":"education","lineage":["https://openalex.org/I206935292"]}],"countries":["SA"],"is_corresponding":true,"raw_author_name":"Saleh Alyahyan","raw_affiliation_strings":["Applied College in Dwadmi, Shaqra University, Shaqra, Saudi Arabia"],"affiliations":[{"raw_affiliation_string":"Applied College in Dwadmi, Shaqra University, Shaqra, Saudi Arabia","institution_ids":["https://openalex.org/I206935292"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5053674667"],"corresponding_institution_ids":["https://openalex.org/I206935292"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07173359,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"57","issue":"4","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":1.0,"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":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.996999979019165,"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":0.9671000242233276,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.9385054111480713},{"id":"https://openalex.org/keywords/computational-intelligence","display_name":"Computational intelligence","score":0.7824976444244385},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.7142941951751709},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6784274578094482},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6625679135322571},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6366745829582214},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5168678760528564},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43462181091308594},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4272872805595398},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3576897978782654}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9385054111480713},{"id":"https://openalex.org/C139502532","wikidata":"https://www.wikidata.org/wiki/Q1122090","display_name":"Computational intelligence","level":2,"score":0.7824976444244385},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.7142941951751709},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6784274578094482},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6625679135322571},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6366745829582214},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5168678760528564},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43462181091308594},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4272872805595398},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3576897978782654},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s11063-025-11777-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-025-11777-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-025-11777-3.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s11063-025-11777-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-025-11777-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-025-11777-3.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.6899999976158142,"display_name":"Climate action"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320324457","display_name":"Shaqra University","ror":"https://ror.org/05hawb687"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411478996.pdf","grobid_xml":"https://content.openalex.org/works/W4411478996.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W2342045095","https://openalex.org/W2768346313","https://openalex.org/W2924566522","https://openalex.org/W2954996726","https://openalex.org/W2965154261","https://openalex.org/W2980214238","https://openalex.org/W3028525609","https://openalex.org/W3083878034","https://openalex.org/W3094117827","https://openalex.org/W3111734603","https://openalex.org/W3194222974","https://openalex.org/W4210624117","https://openalex.org/W4283167468","https://openalex.org/W4291414590","https://openalex.org/W4327969855","https://openalex.org/W4361017517","https://openalex.org/W4377136725","https://openalex.org/W4380084917","https://openalex.org/W4384818316","https://openalex.org/W4390650730","https://openalex.org/W4402208048","https://openalex.org/W4405763074","https://openalex.org/W4408630346","https://openalex.org/W4409156976","https://openalex.org/W6600238479"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4310988119","https://openalex.org/W4285226279","https://openalex.org/W4288019534"],"abstract_inverted_index":{"Deep":[0],"learning":[1,49,98],"has":[2],"revolutionized":[3],"image":[4],"classification":[5],"and":[6,28,73,81,87,107,119,129],"other":[7],"AI-driven":[8],"tasks.":[9],"Yet,":[10],"its":[11],"vulnerability":[12],"to":[13,43,84],"adversarial":[14,35,52,56,65,74,97,110],"attacks":[15],"remains":[16],"a":[17,38,63,124],"critical":[18],"limitation,":[19],"particularly":[20],"in":[21,116],"safety\u2013critical":[22],"domains":[23],"such":[24],"as":[25],"autonomous":[26],"systems":[27],"healthcare.":[29],"This":[30],"study":[31],"proposes":[32],"the":[33,45,94],"Resilient":[34],"network":[36],"(RANet),":[37],"novel":[39],"defence":[40,114],"framework":[41,122],"designed":[42],"enhance":[44],"robustness":[46],"of":[47],"deep":[48],"models":[50],"against":[51],"perturbations.":[53],"RANet":[54,100],"integrates":[55],"resilience":[57],"training":[58,66],"(ART)":[59],"through":[60],"key":[61],"components:":[62],"dedicated":[64],"layer,":[67],"adaptive":[68],"perturbation":[69],"control,":[70],"feature-space":[71],"augmentation,":[72],"dropout.":[75],"These":[76],"modules":[77],"collectively":[78],"improve":[79],"generalization":[80],"reduce":[82],"susceptibility":[83],"both":[85,117],"targeted":[86],"non-targeted":[88],"attacks.":[89],"Evaluated":[90],"on":[91,104,109],"datasets":[92],"from":[93],"NIPS":[95],"2017":[96],"challenge,":[99],"achieves":[101],"92.5%":[102],"accuracy":[103,118],"clean":[105],"data":[106],"75.3%":[108],"data,":[111],"outperforming":[112],"existing":[113],"methods":[115],"robustness.":[120],"The":[121],"demonstrates":[123],"strong":[125],"balance":[126],"between":[127],"performance":[128],"security,":[130],"making":[131],"it":[132],"viable":[133],"for":[134],"real-world":[135],"deployment.":[136]},"counts_by_year":[],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
