{"id":"https://openalex.org/W7105605623","doi":"https://doi.org/10.1109/tmm.2025.3632635","title":"Rectifying Adversarial Sample With Low Entropy Prior for Test-Time Defense","display_name":"Rectifying Adversarial Sample With Low Entropy Prior for Test-Time Defense","publication_year":2025,"publication_date":"2025-11-13","ids":{"openalex":"https://openalex.org/W7105605623","doi":"https://doi.org/10.1109/tmm.2025.3632635"},"language":null,"primary_location":{"id":"doi:10.1109/tmm.2025.3632635","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2025.3632635","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-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":null,"display_name":"Lina Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lina Ma","raw_affiliation_strings":["Chongqing Key Laboratory of Bio-perception and Multimodal Intelligent Information Processing, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Chongqing Key Laboratory of Bio-perception and Multimodal Intelligent Information Processing, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiaowei Fu","orcid":"https://orcid.org/0009-0004-9723-2955"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaowei Fu","raw_affiliation_strings":["Chongqing Key Laboratory of Bio-perception and Multimodal Intelligent Information Processing, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Chongqing Key Laboratory of Bio-perception and Multimodal Intelligent Information Processing, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Fuxiang Huang","orcid":"https://orcid.org/0000-0003-0399-9932"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuxiang Huang","raw_affiliation_strings":["Chongqing Key Laboratory of Bio-perception and Multimodal Intelligent Information Processing, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Chongqing Key Laboratory of Bio-perception and Multimodal Intelligent Information Processing, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xinbo Gao","orcid":"https://orcid.org/0000-0002-7985-0037"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinbo Gao","raw_affiliation_strings":["Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"last","author":{"id":null,"display_name":"Lei Zhang","orcid":"https://orcid.org/0000-0002-5305-8543"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Zhang","raw_affiliation_strings":["Chongqing Key Laboratory of Bio-perception and Multimodal Intelligent Information Processing, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Chongqing Key Laboratory of Bio-perception and Multimodal Intelligent Information Processing, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I158842170"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.78858608,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"1104","last_page":"1118"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9941999912261963,"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":0.9941999912261963,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.000699999975040555,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.00039999998989515007,"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":0.9294999837875366},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.6306999921798706},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4973999857902527},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.43939998745918274},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.3977000117301941},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.35440000891685486}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9294999837875366},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7479000091552734},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.6306999921798706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5080999732017517},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.43939998745918274},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3977000117301941},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3840000033378601},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2881999909877777},{"id":"https://openalex.org/C3020028006","wikidata":"https://www.wikidata.org/wiki/Q9158","display_name":"Electronic mail","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C50942859","wikidata":"https://www.wikidata.org/wiki/Q4967193","display_name":"Rectification","level":3,"score":0.25619998574256897},{"id":"https://openalex.org/C66696666","wikidata":"https://www.wikidata.org/wiki/Q17105612","display_name":"Sample entropy","level":3,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2025.3632635","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2025.3632635","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2123097583","https://openalex.org/W2194775991","https://openalex.org/W2243397390","https://openalex.org/W2302255633","https://openalex.org/W2774644650","https://openalex.org/W2903665338","https://openalex.org/W2954182683","https://openalex.org/W2963743213","https://openalex.org/W2963857521","https://openalex.org/W2964137095","https://openalex.org/W2971626200","https://openalex.org/W3003520923","https://openalex.org/W3035524453","https://openalex.org/W3040306570","https://openalex.org/W3089221115","https://openalex.org/W3107235539","https://openalex.org/W3129948946","https://openalex.org/W3150450875","https://openalex.org/W3161469108","https://openalex.org/W3177410622","https://openalex.org/W3201386741","https://openalex.org/W3208594240","https://openalex.org/W4205145874","https://openalex.org/W4293846201","https://openalex.org/W4313025443","https://openalex.org/W4323896829","https://openalex.org/W4360798603","https://openalex.org/W4367171947","https://openalex.org/W4386083049","https://openalex.org/W4390873191","https://openalex.org/W4393160451","https://openalex.org/W4394585815","https://openalex.org/W4399169033","https://openalex.org/W4402716031"],"related_works":[],"abstract_inverted_index":{"Existing":[0],"defense":[1,117],"methods":[2],"fail":[3],"to":[4,24,105,138,149,210],"defend":[5],"against":[6,60],"un":[7],"known":[8],"attacks":[9,33,62,75],"and":[10,53,81,90],"thus":[11,119,190],"raise":[12],"generalization":[13],"issue":[14],"of":[15,215,231],"adversarial":[16,51,88,135,140,152,166],"robustness.":[17],"To":[18,168],"remedy":[19],"this":[20,37],"problem,":[21],"we":[22,39,120,147,180,203],"attempt":[23],"delve":[25],"into":[26],"some":[27],"underlying":[28],"common":[29],"characteristics":[30],"among":[31],"various":[32,50,74],"for":[34,87,95,133],"generality.":[35],"In":[36],"work,":[38],"reveal":[40],"the":[41,57,106,170,200,213,229],"commonly":[42],"overlooked":[43],"low":[44,84,156,178],"entropy":[45,85,93,157,194,217],"prior":[46,67,112,132],"(LE)":[47],"implied":[48],"in":[49,63,78,102],"samples,":[52,146],"shed":[54],"light":[55],"on":[56,130,199,220],"universal":[58],"robustness":[59],"unseen":[61],"inference":[64],"phase.":[65],"LE":[66,111,131],"is":[68],"elaborated":[69],"as":[70,76],"two":[71],"properties":[72],"across":[73],"shown":[77],"Fig.":[79],"1":[80],"2:":[82],"1)":[83],"misclassification":[86],"samples":[89,141,153,172],"2)":[91],"lower":[92],"prediction":[94,161,188],"higher":[96],"attack":[97,206],"intensity.":[98],"This":[99],"phenomenon":[100],"stands":[101],"stark":[103],"contrast":[104],"naturally":[107],"distributed":[108],"samples.":[109],"The":[110],"can":[113,173,226],"instruct":[114],"existing":[115,232],"test-time":[116,134],"methods,":[118],"propose":[121,148,204],"a":[122,192],"two-stage":[123],"REAL":[124,225],"approach:":[125],"Rectify":[126],"Adversarial":[127],"sample":[128,233],"based":[129,198],"rectification.":[136],"Specifically,":[137],"align":[139],"more":[142],"closely":[143],"with":[144,155,177],"clean":[145],"first":[150],"rectify":[151],"misclassified":[154],"by":[158,185],"reverse":[159],"maximizing":[160],"entropy,":[162,179,189],"thereby":[163],"eliminating":[164],"their":[165],"nature.":[167],"ensure":[169],"rectified":[171],"be":[174],"correctly":[175],"classified":[176],"carry":[181],"out":[182],"secondary":[183],"rectification":[184,234],"forward":[186],"minimizing":[187],"creating":[191],"Max-Min":[193,216],"optimization":[195],"scheme.":[196],"Further,":[197],"second":[201],"property,":[202],"an":[205],"aware":[207],"weighting":[208],"mechanism":[209],"adaptively":[211],"adjust":[212],"strengths":[214],"objectives.":[218],"Experiments":[219],"several":[221],"datasets":[222],"show":[223],"that":[224],"greatly":[227],"improve":[228],"performance":[230],"models.":[235]},"counts_by_year":[],"updated_date":"2026-03-12T06:13:28.667946","created_date":"2025-11-13T00:00:00"}
