{"id":"https://openalex.org/W3083199354","doi":"https://doi.org/10.1109/mwscas48704.2020.9184684","title":"Can Hardware Performance Counters Detect Adversarial Inputs?","display_name":"Can Hardware Performance Counters Detect Adversarial Inputs?","publication_year":2020,"publication_date":"2020-08-01","ids":{"openalex":"https://openalex.org/W3083199354","doi":"https://doi.org/10.1109/mwscas48704.2020.9184684","mag":"3083199354"},"language":"en","primary_location":{"id":"doi:10.1109/mwscas48704.2020.9184684","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwscas48704.2020.9184684","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS)","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/A5056444694","display_name":"Preet Derasari","orcid":"https://orcid.org/0000-0002-2177-393X"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Preet Derasari","raw_affiliation_strings":["School of Engineering and Applied Science, The George Washington University"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Applied Science, The George Washington University","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071177066","display_name":"Siva Koppineedi","orcid":null},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siva Koppineedi","raw_affiliation_strings":["School of Engineering and Applied Science, The George Washington University"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Applied Science, The George Washington University","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045879054","display_name":"Guru Venkataramani","orcid":"https://orcid.org/0000-0002-7084-7560"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guru Venkataramani","raw_affiliation_strings":["School of Engineering and Applied Science, The George Washington University"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Applied Science, The George Washington University","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5056444694"],"corresponding_institution_ids":["https://openalex.org/I193531525"],"apc_list":null,"apc_paid":null,"fwci":0.2651,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63215224,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"945","last_page":"948"},"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/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.996399998664856,"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":0.9855999946594238,"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/computer-science","display_name":"Computer science","score":0.761063814163208},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6504262685775757},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6118234992027283},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.6074921488761902},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5421446561813354},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5212150812149048},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47523120045661926},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46503350138664246},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.4392150044441223},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.41228818893432617},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3866969645023346},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3319319486618042},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07236254215240479},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06823661923408508}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.761063814163208},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6504262685775757},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6118234992027283},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.6074921488761902},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5421446561813354},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5212150812149048},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47523120045661926},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46503350138664246},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.4392150044441223},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.41228818893432617},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3866969645023346},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3319319486618042},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07236254215240479},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06823661923408508},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mwscas48704.2020.9184684","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwscas48704.2020.9184684","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1945616565","https://openalex.org/W2117876524","https://openalex.org/W2607219512","https://openalex.org/W2620038827","https://openalex.org/W2748789698","https://openalex.org/W2765384636","https://openalex.org/W2797142180","https://openalex.org/W2953384591","https://openalex.org/W2963001136","https://openalex.org/W2963207607","https://openalex.org/W2963496101","https://openalex.org/W2963744840","https://openalex.org/W2963946669","https://openalex.org/W2964197269","https://openalex.org/W2964253222","https://openalex.org/W3118608800","https://openalex.org/W4293846201","https://openalex.org/W4293865127","https://openalex.org/W4306369316","https://openalex.org/W6640425456","https://openalex.org/W6713134421","https://openalex.org/W6736207377","https://openalex.org/W6739868092","https://openalex.org/W6743581629","https://openalex.org/W6745272055","https://openalex.org/W6748204703","https://openalex.org/W6750223653","https://openalex.org/W6765694979","https://openalex.org/W6787972765","https://openalex.org/W6846221585"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W4383221314","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W2997056298"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"has":[2,12],"become":[3],"an":[4,76],"integral":[5],"part":[6],"of":[7],"modern-day":[8],"applications.":[9],"Recent":[10],"research":[11],"shown":[13],"how":[14],"inputs":[15,49,97],"for":[16,89],"a":[17,85],"Neural":[18],"Network":[19],"can":[20],"be":[21],"perturbed":[22],"to":[23,30,53,72,108],"disrupt":[24],"its":[25],"detection":[26],"accuracy":[27],"and":[28,95,112],"lead":[29],"fatal":[31],"consequences.":[32],"In":[33],"this":[34],"paper,":[35],"we":[36],"investigate":[37],"whether":[38],"hardware":[39,81],"performance":[40,82,91],"counters":[41,83],"available":[42],"in":[43],"most":[44],"modern":[45],"microprocessors":[46],"uncover":[47],"adversarial":[48,96],"constructed":[50],"using":[51],"perturbations":[52],"the":[54,80],"clean":[55,94,111],"input":[56],"images.":[57],"Our":[58],"experiments":[59],"on":[60,98],"three":[61],"different":[62],"datasets,":[63],"having":[64],"real-life":[65],"DNN":[66],"applications":[67],"ranging":[68],"from":[69],"traffic":[70],"sign":[71],"melanoma":[73],"detectors,":[74],"paints":[75],"interesting":[77],"picture-":[78],"while":[79],"show":[84,104],"difference":[86],"(approximately":[87],"1%":[88],"some":[90],"counters)":[92],"between":[93,110],"individual":[99],"samples,":[100],"they":[101],"do":[102],"not":[103],"any":[105],"significant":[106],"trend":[107],"distinguish":[109],"modified":[113],"samples.":[114]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
