{"id":"https://openalex.org/W4386326782","doi":"https://doi.org/10.1145/3617895","title":"Unifying Gradients to Improve Real-World Robustness for Deep Networks","display_name":"Unifying Gradients to Improve Real-World Robustness for Deep Networks","publication_year":2023,"publication_date":"2023-08-31","ids":{"openalex":"https://openalex.org/W4386326782","doi":"https://doi.org/10.1145/3617895"},"language":"en","primary_location":{"id":"doi:10.1145/3617895","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3617895","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","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":"https://openalex.org/A5043103794","display_name":"Yingwen Wu","orcid":"https://orcid.org/0000-0002-9994-2414"},"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":true,"raw_author_name":"Yingwen Wu","raw_affiliation_strings":["Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China"],"affiliations":[{"raw_affiliation_string":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101888837","display_name":"Sizhe Chen","orcid":"https://orcid.org/0000-0003-3274-6926"},"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":"Sizhe Chen","raw_affiliation_strings":["Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China"],"affiliations":[{"raw_affiliation_string":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101435392","display_name":"Kun Fang","orcid":"https://orcid.org/0000-0001-6351-201X"},"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":"Kun Fang","raw_affiliation_strings":["Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China"],"affiliations":[{"raw_affiliation_string":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026169938","display_name":"Xiaolin Huang","orcid":"https://orcid.org/0000-0003-4285-6520"},"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":"Xiaolin Huang","raw_affiliation_strings":["Institute of Image Processing and Pattern Recognition and the MOE Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, China"],"affiliations":[{"raw_affiliation_string":"Institute of Image Processing and Pattern Recognition and the MOE Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5043103794"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.5219,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.71787287,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":"14","issue":"6","first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":1.0,"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":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9571999907493591,"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/T10645","display_name":"Cardiac Arrest and Resuscitation","score":0.946399986743927,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8930090665817261},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.8664747476577759},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6712653636932373},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5172744393348694},{"id":"https://openalex.org/keywords/threat-model","display_name":"Threat model","score":0.517140805721283},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43836885690689087},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3701406717300415},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.23241132497787476}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8930090665817261},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8664747476577759},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6712653636932373},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5172744393348694},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.517140805721283},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43836885690689087},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3701406717300415},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.23241132497787476},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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:10.1145/3617895","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3617895","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4020255992","display_name":null,"funder_award_id":"Project","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4446273622","display_name":null,"funder_award_id":"11105","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5939423041","display_name":null,"funder_award_id":"Technology","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8081038515","display_name":null,"funder_award_id":"61977046","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8721642152","display_name":null,"funder_award_id":"2021SHZDZX0102","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8955107213","display_name":null,"funder_award_id":"Major","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1996796871","https://openalex.org/W2108598243","https://openalex.org/W2302255633","https://openalex.org/W2603766943","https://openalex.org/W2950787360","https://openalex.org/W2962710014","https://openalex.org/W2963303354","https://openalex.org/W2963485691","https://openalex.org/W2963952467","https://openalex.org/W2964137095","https://openalex.org/W2998421476","https://openalex.org/W3015001695","https://openalex.org/W3035743198","https://openalex.org/W3080260826","https://openalex.org/W3091857398","https://openalex.org/W3107235539","https://openalex.org/W3143848010","https://openalex.org/W3174136778","https://openalex.org/W3190041646","https://openalex.org/W3208993340","https://openalex.org/W3213831029","https://openalex.org/W4226110825","https://openalex.org/W4300072878","https://openalex.org/W4365806382"],"related_works":["https://openalex.org/W2770593030","https://openalex.org/W3210882018","https://openalex.org/W2970990331","https://openalex.org/W4379116102","https://openalex.org/W3207178610","https://openalex.org/W3211782752","https://openalex.org/W3091172437","https://openalex.org/W4386326782","https://openalex.org/W4291960793","https://openalex.org/W4280614064"],"abstract_inverted_index":{"The":[0,222],"wide":[1],"application":[2],"of":[3,12,59,88,183,210,218],"deep":[4],"neural":[5],"networks":[6],"(DNNs)":[7],"demands":[8],"an":[9],"increasing":[10],"amount":[11],"attention":[13],"to":[14,47,62,129,151],"their":[15],"real-world":[16,82,125,167],"robustness,":[17],"i.e.,":[18],"whether":[19],"a":[20,41,54,81,97,131,143,181,187],"DNN":[21],"resists":[22],"black-box":[23],"adversarial":[24],"attacks,":[25],"among":[26],"which":[27,147],"score-based":[28],"query":[29],"attacks":[30,159],"(SQAs)":[31],"are":[32],"the":[33,63,70,120,192,215,219],"most":[34],"threatening":[35],"since":[36],"they":[37],"can":[38],"effectively":[39],"hurt":[40],"victim":[42],"network":[43],"with":[44,74],"only":[45,95,197],"access":[46],"model":[48,182,196,220],"outputs.":[49],"Defending":[50],"against":[51],"SQAs":[52,93],"requires":[53],"slight":[55],"but":[56],"artful":[57],"variation":[58],"outputs":[60],"due":[61],"service":[64],"purpose":[65],"for":[66,105],"users,":[67],"who":[68],"share":[69],"same":[71],"output":[72],"information":[73],"SQAs.":[75],"In":[76],"this":[77],"article,":[78],"we":[79],"propose":[80],"defense":[83,162],"by":[84,127,142],"Unifying":[85],"Gradients":[86],"(UniG)":[87],"different":[89,106],"data":[90],"so":[91],"that":[92,102],"could":[94],"probe":[96],"much":[98],"weaker":[99],"attack":[100,111,136,190],"direction":[101],"is":[103,148,224],"similar":[104],"samples.":[107],"Since":[108],"such":[109],"universal":[110],"perturbations":[112],"have":[113],"been":[114],"validated":[115],"as":[116],"less":[117,134],"aggressive":[118],"than":[119],"input-specific":[121],"perturbations,":[122],"UniG":[123,140,164,179,203],"protects":[124],"DNNs":[126],"indicating":[128],"attackers":[130],"twisted":[132],"and":[133,160,175,213],"informative":[135],"direction.":[137],"We":[138],"implement":[139],"efficiently":[141],"Hadamard":[144],"product":[145],"module,":[146],"plug-and-play.":[149],"According":[150],"extensive":[152],"experiments":[153],"on":[154,173,200],"5":[155],"SQAs,":[156],"2":[157],"adaptive":[158],"7":[161],"baselines,":[163],"significantly":[165],"improves":[166],"robustness":[168],"without":[169],"hurting":[170],"clean":[171,211],"accuracy":[172,185,212],"CIFAR10":[174],"ImageNet.":[176],"For":[177],"instance,":[178],"maintains":[180],"77.80%":[184],"under":[186],"2500-query":[188],"Square":[189],"while":[191],"state-of-the-art":[193],"adversarially":[194],"trained":[195],"has":[198],"67.34%":[199],"CIFAR10.":[201],"Simultaneously,":[202],"outperforms":[204],"all":[205],"compared":[206],"baselines":[207],"in":[208],"terms":[209],"achieves":[214],"smallest":[216],"modification":[217],"output.":[221],"code":[223],"released":[225],"at":[226],"https://github.com/snowien/UniG-pytorch":[227],".":[228]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}