{"id":"https://openalex.org/W4312814809","doi":"https://doi.org/10.1109/ijcnn55064.2022.9891957","title":"Defensive Bit Planes: Defense Against Adversarial Attacks","display_name":"Defensive Bit Planes: Defense Against Adversarial Attacks","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312814809","doi":"https://doi.org/10.1109/ijcnn55064.2022.9891957"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9891957","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9891957","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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/A5005713107","display_name":"Achyut Mani Tripathi","orcid":"https://orcid.org/0000-0003-0548-9688"},"institutions":[{"id":"https://openalex.org/I1317621060","display_name":"Indian Institute of Technology Guwahati","ror":"https://ror.org/0022nd079","country_code":"IN","type":"education","lineage":["https://openalex.org/I1317621060"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Achyut Mani Tripathi","raw_affiliation_strings":["Indian Institute of Technology Guwahati,Department of Computer Science &#x0026; Engineering,Guwahati,Assam,India,781039"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Guwahati,Department of Computer Science &#x0026; Engineering,Guwahati,Assam,India,781039","institution_ids":["https://openalex.org/I1317621060"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014658425","display_name":"Swarup Ranjan Behera","orcid":null},"institutions":[{"id":"https://openalex.org/I1317621060","display_name":"Indian Institute of Technology Guwahati","ror":"https://ror.org/0022nd079","country_code":"IN","type":"education","lineage":["https://openalex.org/I1317621060"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Swarup Ranjan Behera","raw_affiliation_strings":["Indian Institute of Technology Guwahati,Department of Computer Science &#x0026; Engineering,Guwahati,Assam,India,781039"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Guwahati,Department of Computer Science &#x0026; Engineering,Guwahati,Assam,India,781039","institution_ids":["https://openalex.org/I1317621060"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086500792","display_name":"Konark Paul","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Konark Paul","raw_affiliation_strings":["Independent Researcher,Banguluru,Karnataka,India","Independent Researcher, Banguluru, Karnataka, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Independent Researcher,Banguluru,Karnataka,India","institution_ids":[]},{"raw_affiliation_string":"Independent Researcher, Banguluru, Karnataka, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":"1","last_page":"8"},"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.972100019454956,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.9531782865524292},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.9349345564842224},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7488664984703064},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6213257312774658},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6191092729568481},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6055117845535278},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4562285542488098},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4022579789161682}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9531782865524292},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.9349345564842224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7488664984703064},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6213257312774658},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6191092729568481},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6055117845535278},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4562285542488098},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4022579789161682},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9891957","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9891957","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W2007339694","https://openalex.org/W2243397390","https://openalex.org/W2601450892","https://openalex.org/W2892035503","https://openalex.org/W2944090083","https://openalex.org/W2962933288","https://openalex.org/W2963564844","https://openalex.org/W2963587345","https://openalex.org/W2963857521","https://openalex.org/W2964197269","https://openalex.org/W2966535964","https://openalex.org/W2996564870","https://openalex.org/W3033210711","https://openalex.org/W3034190247","https://openalex.org/W3083566224","https://openalex.org/W3107485598","https://openalex.org/W3127986341","https://openalex.org/W3129579680","https://openalex.org/W3134815184","https://openalex.org/W3139065837","https://openalex.org/W3158981988","https://openalex.org/W3174102408","https://openalex.org/W3190601614","https://openalex.org/W4288363925","https://openalex.org/W4293846201","https://openalex.org/W4296406003","https://openalex.org/W4300511536","https://openalex.org/W4392181513","https://openalex.org/W6637162671","https://openalex.org/W6640425456","https://openalex.org/W6719080892","https://openalex.org/W6735236233","https://openalex.org/W6739868092","https://openalex.org/W6748204703","https://openalex.org/W6761100157","https://openalex.org/W6771809012","https://openalex.org/W6778863768","https://openalex.org/W6781475014","https://openalex.org/W6790428421","https://openalex.org/W6795825774"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4389249638","https://openalex.org/W2733410219","https://openalex.org/W2734358244","https://openalex.org/W2901671134"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"models":[2,47],"are":[3],"vulnerable":[4],"to":[5,27,136],"adversarial":[6,11,30,37,53,56,82,141],"examples":[7],"crafted":[8],"with":[9],"different":[10,52],"attack":[12,15],"techniques.":[13],"Numerous":[14],"methods":[16],"have":[17],"been":[18,109],"proposed":[19,85,105,128],"that":[20,48,126],"utilize":[21],"gradient":[22],"information":[23],"of":[24,65,91,97,103],"deep":[25,46,68],"model":[26],"craft":[28],"an":[29,98],"examples.":[31],"Amongst":[32],"the":[33,63,89,104,127],"existing":[34],"defense":[35,79,86,106,138],"mechanisms,":[36],"training":[38,57],"has":[39,108],"gained":[40],"considerable":[41],"attention":[42],"in":[43],"building":[44],"robust":[45,67],"remain":[49],"effective":[50,78],"against":[51,81,140],"attacks.":[54,83,142],"However,":[55],"demands":[58],"high":[59],"computational":[60],"cost":[61],"during":[62],"development":[64],"a":[66,75],"model.":[69],"In":[70],"this":[71],"paper,":[72],"we":[73],"present":[74],"simple":[76],"yet":[77],"mechanism":[80,87],"The":[84,101,121],"uses":[88],"concept":[90],"bit":[92],"plane":[93],"slicing":[94],"for":[95],"de-noising":[96],"input":[99],"image.":[100],"efficacy":[102],"technique":[107,130],"evaluated":[110],"on":[111],"two":[112],"benchmark":[113],"image":[114],"datasets,":[115],"viz.":[116],"MNIST":[117],"and":[118,123,133],"Fashion-MNIST":[119],"datasets.":[120],"experiments":[122],"results":[124],"show":[125],"defence":[129],"yields":[131],"comparable":[132],"competitive":[134],"performance":[135],"state-of-the-art":[137],"techniques":[139]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
