{"id":"https://openalex.org/W4379984504","doi":"https://doi.org/10.1145/3589572.3589598","title":"SARAF: Searching for Adversarial Robust Activation Functions","display_name":"SARAF: Searching for Adversarial Robust Activation Functions","publication_year":2023,"publication_date":"2023-03-10","ids":{"openalex":"https://openalex.org/W4379984504","doi":"https://doi.org/10.1145/3589572.3589598"},"language":"en","primary_location":{"id":"doi:10.1145/3589572.3589598","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589572.3589598","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589572.3589598","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Machine Vision and Applications","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/10.1145/3589572.3589598","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089443492","display_name":"Maghsood Salimi","orcid":"https://orcid.org/0000-0001-5773-1216"},"institutions":[{"id":"https://openalex.org/I82509713","display_name":"M\u00e4lardalen University","ror":"https://ror.org/033vfbz75","country_code":"SE","type":"education","lineage":["https://openalex.org/I82509713"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Maghsood Salimi","raw_affiliation_strings":["School of Innovation, Design and Engineering, Malardalen University, Sweden"],"raw_orcid":"https://orcid.org/0000-0001-5773-1216","affiliations":[{"raw_affiliation_string":"School of Innovation, Design and Engineering, Malardalen University, Sweden","institution_ids":["https://openalex.org/I82509713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001642408","display_name":"Mohammad Loni","orcid":"https://orcid.org/0000-0002-9704-7117"},"institutions":[{"id":"https://openalex.org/I82509713","display_name":"M\u00e4lardalen University","ror":"https://ror.org/033vfbz75","country_code":"SE","type":"education","lineage":["https://openalex.org/I82509713"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Mohammad Loni","raw_affiliation_strings":["School of Innovation, Design and Engineering, Malardalen University, Sweden"],"raw_orcid":"https://orcid.org/0000-0002-9704-7117","affiliations":[{"raw_affiliation_string":"School of Innovation, Design and Engineering, Malardalen University, Sweden","institution_ids":["https://openalex.org/I82509713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078919695","display_name":"Marjan Sirjani","orcid":"https://orcid.org/0000-0001-5478-0987"},"institutions":[{"id":"https://openalex.org/I82509713","display_name":"M\u00e4lardalen University","ror":"https://ror.org/033vfbz75","country_code":"SE","type":"education","lineage":["https://openalex.org/I82509713"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Marjan Sirjani","raw_affiliation_strings":["School of Innovation, Design and Engineering, Malardalen University, Sweden"],"raw_orcid":"https://orcid.org/0000-0001-5478-0987","affiliations":[{"raw_affiliation_string":"School of Innovation, Design and Engineering, Malardalen University, Sweden","institution_ids":["https://openalex.org/I82509713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044187839","display_name":"Antonio Cicchetti","orcid":"https://orcid.org/0000-0003-0416-1787"},"institutions":[{"id":"https://openalex.org/I82509713","display_name":"M\u00e4lardalen University","ror":"https://ror.org/033vfbz75","country_code":"SE","type":"education","lineage":["https://openalex.org/I82509713"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Antonio Cicchetti","raw_affiliation_strings":["School of Innovation, Design and Engineering, Malardalen University, Sweden"],"raw_orcid":"https://orcid.org/0000-0003-0416-1787","affiliations":[{"raw_affiliation_string":"School of Innovation, Design and Engineering, Malardalen University, Sweden","institution_ids":["https://openalex.org/I82509713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008146469","display_name":"Sara Abbaspour Asadollah","orcid":"https://orcid.org/0000-0002-5058-7351"},"institutions":[{"id":"https://openalex.org/I82509713","display_name":"M\u00e4lardalen University","ror":"https://ror.org/033vfbz75","country_code":"SE","type":"education","lineage":["https://openalex.org/I82509713"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Sara Abbaspour Asadollah","raw_affiliation_strings":["School of Innovation, Design and Engineering, Malardalen University, Sweden"],"raw_orcid":"https://orcid.org/0000-0002-5058-7351","affiliations":[{"raw_affiliation_string":"School of Innovation, Design and Engineering, Malardalen University, Sweden","institution_ids":["https://openalex.org/I82509713"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I82509713"],"apc_list":null,"apc_paid":null,"fwci":0.2175,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4027584,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"174","last_page":"182"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9657999873161316,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.957099974155426,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/tweaking","display_name":"Tweaking","score":0.8233652114868164},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8178360462188721},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7286708354949951},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6591958403587341},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.55544114112854},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5455072522163391},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5356501936912537},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4713824689388275},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44407665729522705}],"concepts":[{"id":"https://openalex.org/C2780200862","wikidata":"https://www.wikidata.org/wiki/Q4453309","display_name":"Tweaking","level":2,"score":0.8233652114868164},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8178360462188721},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7286708354949951},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6591958403587341},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.55544114112854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5455072522163391},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5356501936912537},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4713824689388275},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44407665729522705},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3589572.3589598","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589572.3589598","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589572.3589598","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Machine Vision and Applications","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3589572.3589598","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589572.3589598","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589572.3589598","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Machine Vision and Applications","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4379984504.pdf","grobid_xml":"https://content.openalex.org/works/W4379984504.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1977207509","https://openalex.org/W2151363206","https://openalex.org/W2194775991","https://openalex.org/W2201305792","https://openalex.org/W2243397390","https://openalex.org/W2293768274","https://openalex.org/W2884367402","https://openalex.org/W2891828758","https://openalex.org/W2943047381","https://openalex.org/W2963626858","https://openalex.org/W2964081807","https://openalex.org/W2967706363","https://openalex.org/W2970585064","https://openalex.org/W2972268045","https://openalex.org/W2972401431","https://openalex.org/W2981954928","https://openalex.org/W2999552082","https://openalex.org/W3035239274","https://openalex.org/W3038944566","https://openalex.org/W3083251833","https://openalex.org/W3135034047","https://openalex.org/W3163963286","https://openalex.org/W3205297166","https://openalex.org/W3209373101","https://openalex.org/W3211351896","https://openalex.org/W3212981375","https://openalex.org/W3214538500","https://openalex.org/W4283697320","https://openalex.org/W4286901099","https://openalex.org/W4312664201","https://openalex.org/W6802302390"],"related_works":["https://openalex.org/W4241780599","https://openalex.org/W2968428037","https://openalex.org/W1981619058","https://openalex.org/W2505446473","https://openalex.org/W4244708442","https://openalex.org/W2152101763","https://openalex.org/W4323777416","https://openalex.org/W4232914159","https://openalex.org/W4230282030","https://openalex.org/W2286567226"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNNs)":[3],"have":[4],"received":[5],"great":[6],"attention":[7],"in":[8,123],"the":[9,34,52,60,113,124,129,146,164,208],"computer":[10],"vision":[11],"domain.":[12],"However,":[13,107],"CNNs":[14,102],"are":[15,21,27,136],"vulnerable":[16],"to":[17,29,39,83,97,172,204],"adversarial":[18,55,66,117,139,184],"attacks,":[19,185],"which":[20,43],"manipulations":[22],"of":[23,80,116,131,167],"input":[24],"data":[25],"that":[26,135],"imperceptible":[28],"humans":[30],"but":[31],"can":[32,44],"fool":[33],"network.":[35],"Several":[36],"studies":[37],"tried":[38],"address":[40],"this":[41,142],"issue,":[42],"be":[45],"divided":[46],"into":[47],"two":[48],"categories:":[49],"(i)":[50],"training":[51,67,81],"network":[53,61,90,109],"with":[54,151,189],"examples,":[56],"and":[57,175,182],"(ii)":[58],"optimizing":[59,108],"architecture":[62],"and/or":[63],"hyperparameters.":[64],"Although":[65],"is":[68,159],"a":[69,77,85,152,199],"sufficient":[70],"defense":[71],"mechanism,":[72],"they":[73],"suffer":[74,103],"from":[75,104],"requiring":[76],"large":[78],"volume":[79],"samples":[82],"cover":[84],"wide":[86],"perturbation":[87],"bound.":[88],"Tweaking":[89],"activation":[91],"functions":[92],"(AFs)":[93],"has":[94,119],"been":[95,121],"shown":[96],"provide":[98],"promising":[99],"results":[100],"where":[101],"performance":[105],"loss.":[106],"AFs":[110,134,191],"for":[111,133],"compensating":[112],"negative":[114],"impacts":[115],"attacks":[118],"not":[120],"addressed":[122],"literature.":[125],"This":[126,156],"paper":[127],"proposes":[128],"idea":[130],"searching":[132],"robust":[137],"against":[138,179],"attacks.":[140],"To":[141],"aim,":[143],"we":[144],"leverage":[145],"Simulated":[147],"Annealing":[148],"(SA)":[149],"algorithm":[150],"fast":[153],"convergence":[154],"time.":[155],"proposed":[157],"method":[158],"called":[160],"SARAF.":[161],"We":[162],"demonstrate":[163],"consistent":[165],"effectiveness":[166],"SARAF":[168,197],"by":[169],"achieving":[170],"up":[171],"16.92%,":[173],"18.3%,":[174],"15.57%":[176],"accuracy":[177],"improvement":[178],"BIM,":[180],"FGSM,":[181],"PGD":[183],"respectively,":[186],"over":[187],"ResNet-18":[188],"ReLU":[190],"(baseline)":[192],"trained":[193],"on":[194],"CIFAR-10.":[195],"Meanwhile,":[196],"provides":[198],"significant":[200],"search":[201,206],"efficiency":[202],"compared":[203],"random":[205],"as":[207],"optimization":[209],"baseline.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}
