{"id":"https://openalex.org/W7151455171","doi":"https://doi.org/10.48550/arxiv.2604.03693","title":"ResGuard: Enhancing Robustness Against Known Original Attacks in Deep Watermarking","display_name":"ResGuard: Enhancing Robustness Against Known Original Attacks in Deep Watermarking","publication_year":2026,"publication_date":"2026-04-04","ids":{"openalex":"https://openalex.org/W7151455171","doi":"https://doi.org/10.48550/arxiv.2604.03693"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03693","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03693","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.03693","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133084351","display_name":"Hanyi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hanyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133102822","display_name":"Han Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133084659","display_name":"Yupeng Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Yupeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133101390","display_name":"Shilin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133087447","display_name":"Ee-Chien Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang, Ee-Chien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.944599986076355,"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":0.944599986076355,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.00930000003427267,"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":0.009200000204145908,"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/robustness","display_name":"Robustness (evolution)","score":0.8141000270843506},{"id":"https://openalex.org/keywords/digital-watermarking","display_name":"Digital watermarking","score":0.792900025844574},{"id":"https://openalex.org/keywords/watermark","display_name":"Watermark","score":0.6766999959945679},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6665999889373779},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.588699996471405},{"id":"https://openalex.org/keywords/adversary","display_name":"Adversary","score":0.4916999936103821},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4020000100135803},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34040001034736633}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8141000270843506},{"id":"https://openalex.org/C150817343","wikidata":"https://www.wikidata.org/wiki/Q875932","display_name":"Digital watermarking","level":3,"score":0.792900025844574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6995999813079834},{"id":"https://openalex.org/C164112704","wikidata":"https://www.wikidata.org/wiki/Q7974348","display_name":"Watermark","level":3,"score":0.6766999959945679},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6665999889373779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6225000023841858},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.588699996471405},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5181999802589417},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.4916999936103821},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4020000100135803},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35600000619888306},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3398999869823456},{"id":"https://openalex.org/C178489894","wikidata":"https://www.wikidata.org/wiki/Q8789","display_name":"Cryptography","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3357999920845032},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.33239999413490295},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C108801101","wikidata":"https://www.wikidata.org/wiki/Q15032","display_name":"Steganography","level":3,"score":0.31679999828338623},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3052000105381012},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.29580000042915344},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.2705000042915344},{"id":"https://openalex.org/C2987933465","wikidata":"https://www.wikidata.org/wiki/Q141130","display_name":"Image manipulation","level":3,"score":0.26809999346733093},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03693","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03693","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.03693","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03693","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"image":[2,57,104,158],"watermarking":[3],"commonly":[4],"adopts":[5],"an":[6,50,75,161],"\"Encoder-Noise":[7],"Layer-Decoder\"":[8],"(END)":[9],"architecture":[10],"to":[11,54,147,174,196],"improve":[12],"robustness":[13,130],"against":[14],"random":[15],"channel":[16],"distortions,":[17],"yet":[18,41],"it":[19,83],"often":[20],"overlooks":[21],"intentional":[22],"manipulations":[23],"introduced":[24],"by":[25,109,131],"adversaries":[26],"with":[27,151],"additional":[28],"knowledge.":[29],"In":[30],"this":[31,35],"paper,":[32],"we":[33,121],"revisit":[34],"paradigm":[36],"and":[37,81,155],"expose":[38],"a":[39,68,124,139],"critical":[40],"underexplored":[42],"vulnerability:":[43],"the":[44,92,102,172],"Known":[45],"Original":[46],"Attack":[47],"(KOA),":[48],"where":[49],"adversary":[51],"has":[52],"access":[53],"multiple":[55],"original-watermarked":[56],"pairs,":[58],"enabling":[59],"various":[60],"targeted":[61],"suppression":[62],"strategies.":[63],"We":[64],"show":[65],"that":[66,127],"even":[67],"simple":[69],"residual-based":[70],"removal":[71],"approach,":[72],"namely":[73],"estimating":[74],"embedding":[76,179],"residual":[77,140],"from":[78,84,101,194],"known":[79],"pairs":[80],"subtracting":[82],"unseen":[85],"watermarked":[86],"images,":[87],"can":[88],"almost":[89],"completely":[90],"remove":[91],"watermark":[93,191],"while":[94],"preserving":[95],"visual":[96],"quality.":[97],"This":[98],"vulnerability":[99],"stems":[100],"insufficient":[103],"dependency":[105],"of":[106],"residuals":[107,146],"produced":[108],"END":[110],"frameworks,":[111,184],"which":[112,144],"makes":[113],"them":[114],"transferable":[115],"across":[116],"images.":[117],"To":[118],"address":[119],"this,":[120],"propose":[122],"ResGuard,":[123],"plug-and-play":[125],"module":[126],"enhances":[128],"KOA":[129,163,187],"enforcing":[132],"image-dependent":[133],"embedding.":[134],"Its":[135],"core":[136],"lies":[137],"in":[138],"specificity":[141],"enhancement":[142],"loss,":[143],"encourages":[145],"be":[148],"tightly":[149],"coupled":[150],"their":[152],"host":[153],"images":[154],"thus":[156],"improves":[157],"dependency.":[159],"Furthermore,":[160],"auxiliary":[162],"noise":[164],"layer":[165],"injects":[166],"residual-style":[167],"perturbations":[168],"during":[169],"training,":[170],"allowing":[171],"decoder":[173],"remain":[175],"reliable":[176],"under":[177],"stronger":[178],"inconsistencies.":[180],"Integrated":[181],"into":[182],"existing":[183],"ResGuard":[185],"boosts":[186],"robustness,":[188],"improving":[189],"average":[190],"extraction":[192],"accuracy":[193],"59.87%":[195],"99.81%.":[197]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-08T00:00:00"}