{"id":"https://openalex.org/W2785633065","doi":"https://doi.org/10.1109/apsipa.2017.8282152","title":"Robust image identification without any visible information for double-compressed JPEG images","display_name":"Robust image identification without any visible information for double-compressed JPEG images","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2785633065","doi":"https://doi.org/10.1109/apsipa.2017.8282152","mag":"2785633065"},"language":"en","primary_location":{"id":"doi:10.1109/apsipa.2017.8282152","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2017.8282152","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5044004391","display_name":"Kenta Iida","orcid":"https://orcid.org/0000-0001-6288-943X"},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Kenta Iida","raw_affiliation_strings":["Tokyo Metropolitan University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Metropolitan University, Tokyo, Japan","institution_ids":["https://openalex.org/I69740276"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015250468","display_name":"Hitoshi Kiya","orcid":"https://orcid.org/0000-0001-8061-3090"},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hitoshi Kiya","raw_affiliation_strings":["Tokyo Metropolitan University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Metropolitan University, Tokyo, Japan","institution_ids":["https://openalex.org/I69740276"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5044004391"],"corresponding_institution_ids":["https://openalex.org/I69740276"],"apc_list":null,"apc_paid":null,"fwci":0.182,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.59604853,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"12","issue":null,"first_page":"852","last_page":"857"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":1.0,"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"}},{"id":"https://openalex.org/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.9995999932289124,"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"}},{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9965999722480774,"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/jpeg","display_name":"JPEG","score":0.8190065622329712},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7491596341133118},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6996539831161499},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6740992665290833},{"id":"https://openalex.org/keywords/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.6216452121734619},{"id":"https://openalex.org/keywords/lossless-jpeg","display_name":"Lossless JPEG","score":0.5680232048034668},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5439084768295288},{"id":"https://openalex.org/keywords/transform-coding","display_name":"Transform coding","score":0.5224653482437134},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.4734382629394531},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.4467217028141022},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44521504640579224},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.43074122071266174},{"id":"https://openalex.org/keywords/upload","display_name":"Upload","score":0.4181060791015625},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.39041435718536377},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3180733919143677}],"concepts":[{"id":"https://openalex.org/C198751489","wikidata":"https://www.wikidata.org/wiki/Q2195","display_name":"JPEG","level":3,"score":0.8190065622329712},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7491596341133118},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6996539831161499},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6740992665290833},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.6216452121734619},{"id":"https://openalex.org/C8384606","wikidata":"https://www.wikidata.org/wiki/Q2190356","display_name":"Lossless JPEG","level":5,"score":0.5680232048034668},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5439084768295288},{"id":"https://openalex.org/C169805256","wikidata":"https://www.wikidata.org/wiki/Q1361381","display_name":"Transform coding","level":4,"score":0.5224653482437134},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.4734382629394531},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4467217028141022},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44521504640579224},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.43074122071266174},{"id":"https://openalex.org/C71901391","wikidata":"https://www.wikidata.org/wiki/Q7126699","display_name":"Upload","level":2,"score":0.4181060791015625},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39041435718536377},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3180733919143677},{"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/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipa.2017.8282152","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2017.8282152","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6200000047683716,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W129100304","https://openalex.org/W1483739112","https://openalex.org/W1936825185","https://openalex.org/W2059907896","https://openalex.org/W2104722041","https://openalex.org/W2118779391","https://openalex.org/W2149003270","https://openalex.org/W2156410694","https://openalex.org/W2166554499","https://openalex.org/W2183964636","https://openalex.org/W2273141913","https://openalex.org/W2394485828","https://openalex.org/W2396209768","https://openalex.org/W2543412159","https://openalex.org/W2567124838","https://openalex.org/W2577990105","https://openalex.org/W2582467130","https://openalex.org/W2756225977","https://openalex.org/W2785554563","https://openalex.org/W3140272129","https://openalex.org/W4242026686","https://openalex.org/W6605225492","https://openalex.org/W6628898578","https://openalex.org/W6693982343","https://openalex.org/W6712073775","https://openalex.org/W6732561458"],"related_works":["https://openalex.org/W3173684497","https://openalex.org/W3190486427","https://openalex.org/W1830945637","https://openalex.org/W4313377945","https://openalex.org/W2545896937","https://openalex.org/W2111280862","https://openalex.org/W3162084246","https://openalex.org/W2034182554","https://openalex.org/W1993513347","https://openalex.org/W3009281452"],"abstract_inverted_index":{"A":[0],"robust":[1],"identification":[2,43,91],"scheme":[3,98,120],"for":[4,31,101,109],"JPEG":[5,18,72],"images":[6,19,32,46,104,131],"is":[7,14,99,121],"proposed":[8,59,86,119],"in":[9,69,123],"this":[10],"paper.":[11],"The":[12],"aim":[13],"to":[15,34,42,79,105],"robustly":[16],"identify":[17,80],"generated":[20],"from":[21],"the":[22,52,58,61,71,85,102,118,126],"same":[23],"original":[24],"image,":[25],"under":[26],"various":[27],"compression":[28,73],"conditions":[29],"assumed":[30],"uploaded":[33],"social":[35,106],"networks.":[36],"Conventional":[37],"schemes":[38],"are":[39,75,132],"not":[40,50],"applicable":[41],"of":[44,63,66,125],"double-compressed":[45,81],"because":[47],"they":[48],"do":[49],"consider":[51],"errors":[53,74],"caused":[54],"by":[55],"double-compression.":[56],"In":[57,83],"scheme,":[60],"use":[62],"new":[64],"properties":[65],"DCT":[67],"coefficients,":[68],"which":[70],"considered,":[76],"allows":[77],"us":[78],"images.":[82],"addition,":[84],"one":[87],"can":[88],"carry":[89],"out":[90],"without":[92],"using":[93],"any":[94],"visible":[95],"information.":[96],"This":[97],"well-suited":[100],"uploading":[103],"networks":[107],"and":[108,112],"image":[110],"retrieval":[111],"forensics.":[113],"Experimental":[114],"results":[115],"demonstrate":[116],"that":[117],"effective":[122],"terms":[124],"querying":[127],"performance,":[128],"even":[129],"if":[130],"double-":[133],"compressed.":[134]},"counts_by_year":[{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
