{"id":"https://openalex.org/W4385568165","doi":"https://doi.org/10.1145/3580305.3599513","title":"ST-iFGSM: Enhancing Robustness of Human Mobility Signature Identification Model via Spatial-Temporal Iterative FGSM","display_name":"ST-iFGSM: Enhancing Robustness of Human Mobility Signature Identification Model via Spatial-Temporal Iterative FGSM","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385568165","doi":"https://doi.org/10.1145/3580305.3599513"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599513","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599513","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599513","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/10.1145/3580305.3599513","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103162777","display_name":"Mingzhi Hu","orcid":"https://orcid.org/0009-0005-5694-6780"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingzhi Hu","raw_affiliation_strings":["Worcester Polytechnic Institute, Worcester, MA, USA"],"raw_orcid":"https://orcid.org/0009-0005-5694-6780","affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute, Worcester, MA, USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327554","display_name":"Xin Zhang","orcid":"https://orcid.org/0000-0003-0289-1452"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Zhang","raw_affiliation_strings":["Worcester Polytechnic Institute, Worcester, MA, USA"],"raw_orcid":"https://orcid.org/0000-0003-0289-1452","affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute, Worcester, MA, USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100630059","display_name":"Yanhua Li","orcid":"https://orcid.org/0000-0001-8972-503X"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanhua Li","raw_affiliation_strings":["Worcester Polytechnic Institute, Worcester, MA, USA"],"raw_orcid":"https://orcid.org/0000-0001-8972-503X","affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute, Worcester, MA, USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086198510","display_name":"Xun Zhou","orcid":"https://orcid.org/0000-0003-4930-6572"},"institutions":[{"id":"https://openalex.org/I126307644","display_name":"University of Iowa","ror":"https://ror.org/036jqmy94","country_code":"US","type":"education","lineage":["https://openalex.org/I126307644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xun Zhou","raw_affiliation_strings":["University of Iowa, Iowa City, IA, USA"],"raw_orcid":"https://orcid.org/0000-0003-4930-6572","affiliations":[{"raw_affiliation_string":"University of Iowa, Iowa City, IA, USA","institution_ids":["https://openalex.org/I126307644"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106731907","display_name":"Jun Luo","orcid":"https://orcid.org/0000-0002-2032-0381"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Luo","raw_affiliation_strings":["Lenovo Group Limited, Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-2032-0381","affiliations":[{"raw_affiliation_string":"Lenovo Group Limited, Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I4210156165"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8631,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.74148798,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"764","last_page":"774"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9957000017166138,"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":0.9957000017166138,"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/T10751","display_name":"Forensic and Genetic Research","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10800","display_name":"Forensic Toxicology and Drug Analysis","score":0.9897000193595886,"subfield":{"id":"https://openalex.org/subfields/3005","display_name":"Toxicology"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7901694178581238},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7899491786956787},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5235254764556885},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4504556953907013},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.4128960967063904},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4090757966041565},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3441043496131897}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7901694178581238},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7899491786956787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5235254764556885},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4504556953907013},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.4128960967063904},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4090757966041565},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3441043496131897},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599513","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599513","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599513","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3580305.3599513","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599513","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599513","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3387330441","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320337391","funder_display_name":"Division of Civil, Mechanical and Manufacturing Innovation"},{"id":"https://openalex.org/G5786289080","display_name":"SCC-IRG Track 1: Empowering and Enhancing Workers Through Building A Community-Centered Gig Economy","funder_award_id":"1952085","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7832241836","display_name":"NRT-HDR: Data-Driven Sustainable Engineering for a Circular Economy","funder_award_id":"2021871","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8372766862","display_name":"SCC: Leveraging Autonomous Shared Vehicles for Greater Community Health, Equity, Livability, and Prosperity (HELP)","funder_award_id":"1831140","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8385489419","display_name":null,"funder_award_id":"69A3551747131","funder_id":"https://openalex.org/F4320306108","funder_display_name":"U.S. Department of Transportation"},{"id":"https://openalex.org/G8434345791","display_name":"CAREER: Spatial-Temporal Imitation Learning","funder_award_id":"1942680","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8761899520","display_name":null,"funder_award_id":"1831140","funder_id":"https://openalex.org/F4320337391","funder_display_name":"Division of Civil, Mechanical and Manufacturing Innovation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306108","display_name":"U.S. Department of Transportation","ror":"https://ror.org/02xfw2e90"},{"id":"https://openalex.org/F4320337391","display_name":"Division of Civil, Mechanical and Manufacturing Innovation","ror":"https://ror.org/028yd4c30"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385568165.pdf","grobid_xml":"https://content.openalex.org/works/W4385568165.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W1550669880","https://openalex.org/W1982391657","https://openalex.org/W2025766355","https://openalex.org/W2089953722","https://openalex.org/W2138198492","https://openalex.org/W2161581167","https://openalex.org/W2243397390","https://openalex.org/W2269778407","https://openalex.org/W2387506654","https://openalex.org/W2538506242","https://openalex.org/W2543927648","https://openalex.org/W2547318247","https://openalex.org/W2567442064","https://openalex.org/W2739060064","https://openalex.org/W2765424254","https://openalex.org/W2808862972","https://openalex.org/W2888233014","https://openalex.org/W2897668826","https://openalex.org/W2917779306","https://openalex.org/W2962818281","https://openalex.org/W2963535483","https://openalex.org/W2963857521","https://openalex.org/W2964082701","https://openalex.org/W2964107195","https://openalex.org/W2964159205","https://openalex.org/W2982109374","https://openalex.org/W2998254302","https://openalex.org/W3020242586","https://openalex.org/W3080657898","https://openalex.org/W3103557498","https://openalex.org/W3135147155","https://openalex.org/W3206597993","https://openalex.org/W4206573600","https://openalex.org/W4254635580"],"related_works":["https://openalex.org/W3162200841","https://openalex.org/W2586280620","https://openalex.org/W2805505483","https://openalex.org/W2384744344","https://openalex.org/W1799694159","https://openalex.org/W2393169196","https://openalex.org/W2366610330","https://openalex.org/W1550496571","https://openalex.org/W2558515415","https://openalex.org/W253106158"],"abstract_inverted_index":{"The":[0,38,194],"Human":[1],"Mobility":[2],"Signature":[3],"Identification":[4],"(HuMID)":[5],"problem":[6,40],"aims":[7],"at":[8],"determining":[9],"whether":[10],"the":[11,21,128,132,145,159,177,192,207,220,223],"incoming":[12],"trajectories":[13,24],"were":[14],"generated":[15,195],"by":[16],"a":[17,26,47,113,182,188],"claimed":[18],"agent":[19],"from":[20,212],"historical":[22],"movement":[23],"of":[25,28,50,134,149,185,191],"set":[27],"individual":[29],"human":[30,93],"agents":[31],"such":[32,53],"as":[33,54,103,200],"pedestrians":[34],"and":[35,43,71,130,147,161,205],"taxi":[36,141],"drivers.":[37],"HuMID":[39,80,135,164,178,208],"is":[41,98],"significant,":[42],"its":[44],"solutions":[45],"have":[46],"wide":[48],"range":[49],"real-world":[51,140],"applications,":[52],"criminal":[55],"identification":[56],"for":[57,62],"police":[58],"departments,":[59],"risk":[60],"assessment":[61],"auto":[63],"insurance":[64],"providers,":[65],"driver":[66],"verification":[67],"in":[68,90,108,187],"ride-sharing":[69],"services,":[70],"so":[72],"on.":[73],"Though":[74],"Deep":[75],"neural":[76],"networks":[77],"(DNN)":[78],"based":[79],"models":[81,179],"on":[82,157,216,222],"spatial-temporal":[83],"mobility":[84,95],"fingerprint":[85],"similarity":[86],"demonstrate":[87,144],"remarkable":[88],"performance":[89],"effectively":[91],"identifying":[92],"agents'":[94],"signatures,":[96],"it":[97],"vulnerable":[99],"to":[100,126,175,203,214],"adversarial":[101],"attacks":[102,174,196],"other":[104],"DNN-based":[105],"models.":[106,136],"Therefore,":[107],"this":[109],"paper,":[110],"we":[111],"propose":[112],"Spatial-Temporal":[114],"iterative":[115],"Fast":[116],"Gradient":[117],"Sign":[118],"Method":[119],"with":[120,139,180],"L0":[121],"regularization":[122],"-":[123,125],"ST-iFGSM":[124,151,170],"detect":[127],"vulnerability":[129],"enhance":[131],"robustness":[133],"Extensive":[137],"experiments":[138],"trajectory":[142],"data":[143,202],"efficiency":[146],"effectiveness":[148],"our":[150,155],"algorithm.":[152],"We":[153],"tested":[154],"method":[156],"both":[158],"ST-SiameseNet":[160],"an":[162],"LSTM-based":[163],"classification":[165],"model.":[166],"It":[167],"shows":[168],"that":[169],"can":[171,197],"generate":[172],"successful":[173],"fool":[176],"only":[181],"few":[183],"steps":[184],"attack":[186],"small":[189],"portion":[190],"trajectories.":[193],"be":[198],"used":[199],"augmented":[201],"update":[204],"improve":[206],"model":[209],"accuracy":[210],"significantly":[211],"47.36%":[213],"76.18%":[215],"testing":[217,225],"samples":[218],"after":[219],"attack(86.25%":[221],"original":[224],"samples).":[226]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}
