{"id":"https://openalex.org/W4384209516","doi":"https://doi.org/10.1145/3591569.3591603","title":"A Study on the Optimization of the CNNs for Adversarial Attacks","display_name":"A Study on the Optimization of the CNNs for Adversarial Attacks","publication_year":2023,"publication_date":"2023-02-24","ids":{"openalex":"https://openalex.org/W4384209516","doi":"https://doi.org/10.1145/3591569.3591603"},"language":"en","primary_location":{"id":"doi:10.1145/3591569.3591603","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3591569.3591603","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3591569.3591603","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 8th International Conference on Intelligent Information Technology","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3591569.3591603","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072025200","display_name":"Jongweon Kim","orcid":"https://orcid.org/0000-0002-8916-6431"},"institutions":[{"id":"https://openalex.org/I157264075","display_name":"Sangmyung University","ror":"https://ror.org/01x4whx42","country_code":"KR","type":"education","lineage":["https://openalex.org/I157264075"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongweon Kim","raw_affiliation_strings":["Department of Intelligent Internet of Things, Sangmyung University, Korea"],"raw_orcid":"https://orcid.org/0000-0002-8916-6431","affiliations":[{"raw_affiliation_string":"Department of Intelligent Internet of Things, Sangmyung University, Korea","institution_ids":["https://openalex.org/I157264075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018270572","display_name":"Hyeongcheol Park","orcid":"https://orcid.org/0009-0000-4367-7992"},"institutions":[{"id":"https://openalex.org/I157264075","display_name":"Sangmyung University","ror":"https://ror.org/01x4whx42","country_code":"KR","type":"education","lineage":["https://openalex.org/I157264075"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyeongcheol Park","raw_affiliation_strings":["Department of Converged Electronic Engineering, Sangmyung University, Korea"],"raw_orcid":"https://orcid.org/0009-0000-4367-7992","affiliations":[{"raw_affiliation_string":"Department of Converged Electronic Engineering, Sangmyung University, Korea","institution_ids":["https://openalex.org/I157264075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157264075"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07098181,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"193","last_page":"198"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9944000244140625,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.8911535739898682},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.8803669214248657},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7976431846618652},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7654603719711304},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7296469211578369},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6905941367149353},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5361226797103882},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5045613050460815},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4948689341545105},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47810882329940796},{"id":"https://openalex.org/keywords/gabor-filter","display_name":"Gabor filter","score":0.46391168236732483},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4155537188053131},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.37752318382263184}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8911535739898682},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8803669214248657},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7976431846618652},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7654603719711304},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7296469211578369},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6905941367149353},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5361226797103882},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5045613050460815},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4948689341545105},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47810882329940796},{"id":"https://openalex.org/C2779883129","wikidata":"https://www.wikidata.org/wiki/Q2447890","display_name":"Gabor filter","level":3,"score":0.46391168236732483},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4155537188053131},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.37752318382263184},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3591569.3591603","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3591569.3591603","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3591569.3591603","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 8th International Conference on Intelligent Information Technology","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3591569.3591603","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3591569.3591603","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3591569.3591603","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 8th International Conference on Intelligent Information Technology","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G952882610","display_name":null,"funder_award_id":"NRF-2020RIA2C1101938","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4384209516.pdf","grobid_xml":"https://content.openalex.org/works/W4384209516.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W2007339694","https://openalex.org/W2067713319","https://openalex.org/W2108598243","https://openalex.org/W2116360511","https://openalex.org/W2618530766","https://openalex.org/W3035475567","https://openalex.org/W4285055032"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W4383221314","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W2997056298"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNNs)":[3],"have":[4],"shown":[5],"high":[6],"accuracy":[7],"in":[8,103,139,171],"image":[9],"classification":[10,133],"tasks,":[11],"including":[12],"on":[13,78],"popular":[14],"datasets":[15],"like":[16],"MNIST":[17],"and":[18,73,81],"ImageNet.":[19],"However,":[20],"these":[21],"models":[22,71,122],"can":[23,45],"be":[24,46],"easily":[25],"fooled":[26],"by":[27],"small":[28,158],"perturbations":[29],"added":[30,47],"to":[31,48,55,59,116,123,154,157],"the":[32,39,49,67,84,89,96,118,132,163],"input":[33],"image.":[34],"To":[35],"address":[36],"this":[37,63],"issue,":[38],"VOneBlock":[40,75,87,104,136,151,167],"architecture":[41],"was":[42,152],"proposed,":[43],"which":[44],"frontend":[50],"of":[51,69,86,98,120],"a":[52],"CNN-based":[53],"model":[54,134],"improve":[56,155],"its":[57,106],"robustness":[58,119,156],"adversarial":[60,79,124,147],"attacks.":[61],"In":[62],"paper,":[64],"we":[65,93,111,129,160],"compare":[66],"performance":[68],"CNN":[70,121],"with":[72,135,166],"without":[74],"when":[76],"fine-tuning":[77],"datasets,":[80],"show":[82],"how":[83,95],"inclusion":[85],"affects":[88,105],"model's":[90],"robustness.":[91],"Additionally,":[92],"investigate":[94],"number":[97],"Gabor":[99],"filter":[100],"kernels":[101],"used":[102],"performance.":[107],"Through":[108],"our":[109],"experiments,":[110],"present":[112],"an":[113],"optimal":[114],"way":[115],"enhance":[117],"attacks":[125],"using":[126],"VOneBlock.":[127],"Finally,":[128],"evaluate":[130],"whether":[131],"performs":[137,168],"well":[138,145],"classifying":[140,172],"real-world":[141,173],"attacked":[142,148,174],"images,":[143],"as":[144,146],"images.":[149,175],"While":[150],"developed":[153],"perturbations,":[159],"find":[161],"that":[162],"neural":[164],"network":[165],"slightly":[169],"better":[170]},"counts_by_year":[],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}