{"id":"https://openalex.org/W4312498118","doi":"https://doi.org/10.1109/mmsp55362.2022.9948798","title":"HF-Defend: Defending Against Adversarial Examples Based on Halftoning","display_name":"HF-Defend: Defending Against Adversarial Examples Based on Halftoning","publication_year":2022,"publication_date":"2022-09-26","ids":{"openalex":"https://openalex.org/W4312498118","doi":"https://doi.org/10.1109/mmsp55362.2022.9948798"},"language":"en","primary_location":{"id":"doi:10.1109/mmsp55362.2022.9948798","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp55362.2022.9948798","pdf_url":null,"source":{"id":"https://openalex.org/S4363605768","display_name":"2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)","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 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"},"type":"article","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/A5071460436","display_name":"Gaozhi Liu","orcid":"https://orcid.org/0009-0008-2813-0452"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Gaozhi Liu","raw_affiliation_strings":["School of Computer Science, Fudan University"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100359821","display_name":"Sheng Li","orcid":"https://orcid.org/0000-0002-7932-9831"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Li","raw_affiliation_strings":["School of Computer Science, Fudan University"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036715206","display_name":"Zhenxing Qian","orcid":"https://orcid.org/0000-0002-5224-6374"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenxing Qian","raw_affiliation_strings":["School of Computer Science, Fudan University"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071724015","display_name":"Xinpeng Zhang","orcid":"https://orcid.org/0000-0001-5867-1315"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinpeng Zhang","raw_affiliation_strings":["School of Computer Science, Fudan University"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5071460436"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14932931,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"311","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9983000159263611,"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/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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":0.8773670196533203},{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.8330252766609192},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7345331311225891},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6755292415618896},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6405126452445984},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6309898495674133},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6088348627090454},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5066088438034058}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8773670196533203},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.8330252766609192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7345331311225891},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6755292415618896},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6405126452445984},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6309898495674133},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6088348627090454},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5066088438034058}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mmsp55362.2022.9948798","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp55362.2022.9948798","pdf_url":null,"source":{"id":"https://openalex.org/S4363605768","display_name":"2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)","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 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5699999928474426,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1673923490","https://openalex.org/W1883420340","https://openalex.org/W1945616565","https://openalex.org/W2108598243","https://openalex.org/W2162402353","https://openalex.org/W2194775991","https://openalex.org/W2243397390","https://openalex.org/W2508457857","https://openalex.org/W2561498661","https://openalex.org/W2607219512","https://openalex.org/W2612372205","https://openalex.org/W2617106563","https://openalex.org/W2693668331","https://openalex.org/W2750384547","https://openalex.org/W2765424254","https://openalex.org/W2774018344","https://openalex.org/W2962759300","https://openalex.org/W2962872506","https://openalex.org/W2963389226","https://openalex.org/W2963686971","https://openalex.org/W2963771536","https://openalex.org/W2963857521","https://openalex.org/W2974739971","https://openalex.org/W3004298045","https://openalex.org/W3092991443","https://openalex.org/W3103557498","https://openalex.org/W3118608800","https://openalex.org/W4256044039","https://openalex.org/W4293846201","https://openalex.org/W6620707391","https://openalex.org/W6637162671","https://openalex.org/W6639568328","https://openalex.org/W6640425456","https://openalex.org/W6729756640","https://openalex.org/W6731039222","https://openalex.org/W6736207377","https://openalex.org/W6737443998","https://openalex.org/W6739868092","https://openalex.org/W6743688258","https://openalex.org/W6744679260"],"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/W3153082147","https://openalex.org/W2899689856","https://openalex.org/W2968833425"],"abstract_inverted_index":{"How":[0],"to":[1,22,97,105],"deal":[2],"with":[3],"the":[4,25,32,38,50,64,75,79,86,99,111,119,125],"adversarial":[5,26,39],"examples":[6,27],"attracts":[7],"a":[8,19,43,54,58,71,93,103,107],"lot":[9],"of":[10,49,77,85,113,121],"interest":[11],"recently.":[12],"In":[13,88],"this":[14],"paper,":[15],"we":[16,69,90],"propose":[17,70],"HF-Defend:":[18],"novel":[20],"method":[21],"defend":[23],"against":[24],"based":[28],"on":[29],"halftoning.":[30],"Unlike":[31],"existing":[33,126],"schemes,":[34],"HF-Defend":[35,123],"thoroughly":[36],"removes":[37],"perturbations":[40],"by":[41],"transforming":[42],"8-bit":[44],"grayscale":[45],"image":[46,65],"(or":[47],"one":[48],"RGB":[51],"channels":[52],"in":[53],"color":[55],"image)":[56],"into":[57],"1-bit":[59],"halftoned":[60],"image.":[61,87],"To":[62],"maintain":[63],"quality":[66],"and":[67,82,102],"content,":[68],"reconstruction":[72,112],"module":[73],"for":[74,110],"recovery":[76],"both":[78],"main":[80,100],"contents":[81],"fine":[83,114],"details":[84],"particular,":[89],"newly":[91],"design":[92],"nonlinear":[94],"low-pass":[95],"filter":[96,109],"extract":[98],"contents,":[101],"FilterNet":[104],"establish":[106],"high-pass":[108],"details.":[115],"Experimental":[116],"results":[117],"demonstrate":[118],"advantage":[120],"our":[122],"over":[124],"schemes.":[127]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}