{"id":"https://openalex.org/W4415707872","doi":"https://doi.org/10.1109/icme59968.2025.11209829","title":"Bridging the Gap: Balancing Human Perception and Detector Attention in Adversarial Attacks","display_name":"Bridging the Gap: Balancing Human Perception and Detector Attention in Adversarial Attacks","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415707872","doi":"https://doi.org/10.1109/icme59968.2025.11209829"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11209829","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209829","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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/A5076218215","display_name":"Mingye Xie","orcid":"https://orcid.org/0000-0001-9826-9806"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingye Xie","raw_affiliation_strings":["Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027871520","display_name":"Suncheng Xiang","orcid":"https://orcid.org/0000-0002-9141-6460"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Suncheng Xiang","raw_affiliation_strings":["Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003889945","display_name":"Xian Gao","orcid":"https://orcid.org/0000-0002-5301-0515"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xian Gao","raw_affiliation_strings":["Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418165","display_name":"Ting Liu","orcid":"https://orcid.org/0000-0003-3489-4578"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Liu","raw_affiliation_strings":["Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101704886","display_name":"Yuzhuo Fu","orcid":"https://orcid.org/0000-0001-5516-3016"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuzhuo Fu","raw_affiliation_strings":["Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15793812,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"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":0.9373999834060669,"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":0.9373999834060669,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.012500000186264515,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.007600000128149986,"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/camouflage","display_name":"Camouflage","score":0.8621000051498413},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8299000263214111},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.7172999978065491},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.48089998960494995},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4542999863624573},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4207000136375427}],"concepts":[{"id":"https://openalex.org/C2776196576","wikidata":"https://www.wikidata.org/wiki/Q196113","display_name":"Camouflage","level":2,"score":0.8621000051498413},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8299000263214111},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7487000226974487},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.7172999978065491},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5449000000953674},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.48089998960494995},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4542999863624573},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4207000136375427},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3837999999523163},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3571000099182129},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3547999858856201},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C2780626000","wikidata":"https://www.wikidata.org/wiki/Q5936775","display_name":"Human-in-the-loop","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2635999917984009}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11209829","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209829","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1497599070","https://openalex.org/W1861492603","https://openalex.org/W1934890906","https://openalex.org/W1985538963","https://openalex.org/W2160613239","https://openalex.org/W2736285137","https://openalex.org/W2798302089","https://openalex.org/W2963527086","https://openalex.org/W2997066571","https://openalex.org/W3017485054","https://openalex.org/W3185095134","https://openalex.org/W3199495496","https://openalex.org/W3200450146","https://openalex.org/W3204155906","https://openalex.org/W3205672068","https://openalex.org/W3216289112","https://openalex.org/W4221139075","https://openalex.org/W4320882911","https://openalex.org/W4362474101","https://openalex.org/W4386065718","https://openalex.org/W4390873750","https://openalex.org/W4398756379","https://openalex.org/W4404612908"],"related_works":[],"abstract_inverted_index":{"Adversarial":[0],"attacks":[1,99],"on":[2,14,100,124],"deep":[3],"neural":[4],"networks":[5],"have":[6],"garnered":[7],"significant":[8],"attention,":[9],"with":[10,82,87],"recent":[11],"studies":[12],"focusing":[13],"generating":[15],"intricate,":[16],"colorful":[17],"patterns":[18,25],"designed":[19],"to":[20,30,71,90,120],"mislead":[21],"models.":[22],"However,":[23],"such":[24],"are":[26],"often":[27],"easily":[28],"discernible":[29],"human":[31,48,73,125,139],"observers.":[32],"To":[33],"address":[34],"this":[35],"limitation,":[36],"we":[37,111],"propose":[38],"a":[39,57,78,83,135],"novel":[40],"adversarial":[41,93],"camouflage":[42,105],"framework":[43,133],"BHD":[44],"that":[45,131],"simultaneously":[46],"mitigates":[47],"perceptibility":[49],"and":[50,141],"reduces":[51],"detector":[52],"attention.":[53],"Our":[54],"approach":[55],"defines":[56],"base":[58],"pattern":[59],"by":[60],"leveraging":[61],"the":[62,72,92,122],"surroundings":[63],"of":[64],"target":[65],"object,":[66],"making":[67],"it":[68],"less":[69],"conspicuous":[70],"eye.":[74],"We":[75],"further":[76],"design":[77],"loss":[79],"function,":[80],"integrated":[81],"pre-trained":[84],"vision":[85],"encoder":[86],"fine-tuned":[88],"projector,":[89],"optimize":[91],"pattern.":[94],"This":[95],"allows":[96],"for":[97],"effective":[98],"detectors":[101],"while":[102],"ensuring":[103],"robust":[104],"across":[106],"diverse":[107],"environments.":[108],"In":[109],"addition,":[110],"incorporate":[112],"human-aligned":[113],"large":[114],"multimodal":[115],"models":[116],"as":[117],"objective":[118],"metrics":[119],"quantify":[121],"impact":[123],"perception.":[126],"Extensive":[127],"experimental":[128],"results":[129],"demonstrate":[130],"our":[132],"achieves":[134],"superior":[136],"balance":[137],"between":[138],"imperceptibility":[140],"model":[142],"deception,":[143],"outperforming":[144],"state-of-the-art":[145],"methods.":[146]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-30T00:00:00"}
