{"id":"https://openalex.org/W4308233694","doi":"https://doi.org/10.1109/icip46576.2022.9897931","title":"Hiding Images Into Images with Real-World Robustness","display_name":"Hiding Images Into Images with Real-World Robustness","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308233694","doi":"https://doi.org/10.1109/icip46576.2022.9897931"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897931","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897931","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","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/A5004724820","display_name":"Qichao Ying","orcid":"https://orcid.org/0000-0002-6527-2424"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qichao Ying","raw_affiliation_strings":["Fudan University"],"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025286392","display_name":"Hang Zhou","orcid":"https://orcid.org/0000-0001-7860-8452"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Hang Zhou","raw_affiliation_strings":["Simon Fraser University"],"affiliations":[{"raw_affiliation_string":"Simon Fraser University","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017243961","display_name":"Xianhan Zeng","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianhan Zeng","raw_affiliation_strings":["Fudan University"],"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089057189","display_name":"Haisheng Xu","orcid":"https://orcid.org/0000-0002-5853-3374"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haisheng Xu","raw_affiliation_strings":["NVIDIA"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036715206","display_name":"Zhenxing Qian","orcid":"https://orcid.org/0000-0002-5224-6374"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenxing Qian","raw_affiliation_strings":["Fudan University"],"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071724015","display_name":"Xinpeng Zhang","orcid":"https://orcid.org/0000-0001-5867-1315"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinpeng Zhang","raw_affiliation_strings":["Fudan University"],"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5004724820"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.8388,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.81745919,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"111","last_page":"115"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9997000098228455,"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":0.9997000098228455,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","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"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9940000176429749,"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/computer-science","display_name":"Computer science","score":0.8439978361129761},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7823144197463989},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7026139497756958},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6466667652130127},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5723551511764526},{"id":"https://openalex.org/keywords/jpeg","display_name":"JPEG","score":0.44803640246391296},{"id":"https://openalex.org/keywords/digital-watermarking","display_name":"Digital watermarking","score":0.44770434498786926},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44082748889923096},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.4164491891860962},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.40644940733909607},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3575640320777893}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8439978361129761},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7823144197463989},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7026139497756958},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6466667652130127},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5723551511764526},{"id":"https://openalex.org/C198751489","wikidata":"https://www.wikidata.org/wiki/Q2195","display_name":"JPEG","level":3,"score":0.44803640246391296},{"id":"https://openalex.org/C150817343","wikidata":"https://www.wikidata.org/wiki/Q875932","display_name":"Digital watermarking","level":3,"score":0.44770434498786926},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44082748889923096},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.4164491891860962},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40644940733909607},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3575640320777893},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897931","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897931","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6899999976158142}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1901129140","https://openalex.org/W2192227561","https://openalex.org/W2560266880","https://openalex.org/W2593414223","https://openalex.org/W2607126301","https://openalex.org/W2623100815","https://openalex.org/W2883233582","https://openalex.org/W2890904214","https://openalex.org/W2896101932","https://openalex.org/W2899413106","https://openalex.org/W2901329636","https://openalex.org/W2908539050","https://openalex.org/W2908542906","https://openalex.org/W2919023666","https://openalex.org/W2952122856","https://openalex.org/W2963073614","https://openalex.org/W2963462425","https://openalex.org/W2963857521","https://openalex.org/W2997526307","https://openalex.org/W3167132838","https://openalex.org/W3173104782","https://openalex.org/W3193750732","https://openalex.org/W3206188380","https://openalex.org/W4212774754","https://openalex.org/W4307823382","https://openalex.org/W6631190155","https://openalex.org/W6746523225","https://openalex.org/W6796618255"],"related_works":["https://openalex.org/W2361629745","https://openalex.org/W2107922825","https://openalex.org/W3094285444","https://openalex.org/W1568204688","https://openalex.org/W2381262728","https://openalex.org/W2111592878","https://openalex.org/W1846726187","https://openalex.org/W1593964766","https://openalex.org/W2387614453","https://openalex.org/W2743579231"],"abstract_inverted_index":{"The":[0,69],"existing":[1],"image":[2,66,80],"embedding":[3,52],"networks":[4],"are":[5,142],"basically":[6],"vulnerable":[7],"to":[8,75,145],"malicious":[9],"attacks":[10,118],"such":[11],"as":[12,123,125],"JPEG":[13],"compression":[14],"and":[15,64,99],"noise":[16],"adding,":[17],"not":[18],"applicable":[19],"for":[20,37,94],"real-world":[21,103],"copyright":[22],"protection":[23],"tasks.":[24],"To":[25],"solve":[26],"this":[27],"problem,":[28],"we":[29,141],"introduce":[30],"a":[31,61,120],"generative":[32],"deep":[33],"network":[34,53,63,73],"based":[35],"method":[36,114],"hiding":[38],"images":[39,41,132],"into":[40],"while":[42],"assuring":[43],"high-quality":[44],"extraction":[45,67],"from":[46,81],"the":[47,77,82,88,91,109,112,126,130,134,143],"destructive":[48],"synthesized":[49],"images.":[50,150],"An":[51],"is":[54],"sequentially":[55],"concatenated":[56],"with":[57,133],"an":[58,65],"attack":[59,104],"layer,":[60],"decoupling":[62,72],"network.":[68],"addition":[70],"of":[71,90,111,129,136],"learns":[74],"extract":[76],"embedded":[78],"secret":[79,149],"attacked":[83],"image.":[84],"We":[85],"also":[86],"pinpoint":[87],"weaknesses":[89],"adversarial":[92],"training":[93],"robustness":[95],"in":[96],"previous":[97],"works":[98],"build":[100],"our":[101],"improved":[102],"simulator.":[105],"Experimental":[106],"results":[107],"demonstrate":[108],"superiority":[110],"proposed":[113],"against":[115],"typical":[116],"digital":[117],"by":[119],"large":[121],"margin,":[122],"well":[124],"performance":[127],"boost":[128],"recovered":[131],"aid":[135],"progressive":[137],"recovery":[138],"strategy.":[139],"Besides,":[140],"first":[144],"robustly":[146],"hide":[147],"three":[148]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}