{"id":"https://openalex.org/W7159041073","doi":"https://doi.org/10.48550/arxiv.2604.26519","title":"GIFGuard: Proactive Forensics against Deepfakes in Facial GIFs via Spatiotemporal Watermarking","display_name":"GIFGuard: Proactive Forensics against Deepfakes in Facial GIFs via Spatiotemporal Watermarking","publication_year":2026,"publication_date":"2026-04-29","ids":{"openalex":"https://openalex.org/W7159041073","doi":"https://doi.org/10.48550/arxiv.2604.26519"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.26519","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26519","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.2604.26519","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134898785","display_name":"Shupeng Che","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Che, Shupeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134898344","display_name":"Zhiqing Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Zhiqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134885023","display_name":"Changtao Miao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miao, Changtao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134892781","display_name":"Dan Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Dan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134895102","display_name":"Gaobo Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Gaobo","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/T12357","display_name":"Digital Media Forensic Detection","score":0.6169999837875366,"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":0.6169999837875366,"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":0.19259999692440033,"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":0.12120000272989273,"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":0.7508999705314636},{"id":"https://openalex.org/keywords/digital-watermarking","display_name":"Digital watermarking","score":0.7211999893188477},{"id":"https://openalex.org/keywords/sketch","display_name":"Sketch","score":0.4643999934196472},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4462999999523163},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.43059998750686646},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3767000138759613},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.3702000081539154},{"id":"https://openalex.org/keywords/watermark","display_name":"Watermark","score":0.3630000054836273},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.3497999906539917}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8652999997138977},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7508999705314636},{"id":"https://openalex.org/C150817343","wikidata":"https://www.wikidata.org/wiki/Q875932","display_name":"Digital watermarking","level":3,"score":0.7211999893188477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6381000280380249},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.4643999934196472},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4462999999523163},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3935000002384186},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C164112704","wikidata":"https://www.wikidata.org/wiki/Q7974348","display_name":"Watermark","level":3,"score":0.3630000054836273},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3091999888420105},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C3073032","wikidata":"https://www.wikidata.org/wiki/Q15912075","display_name":"Information hiding","level":3,"score":0.3018999993801117},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C77660652","wikidata":"https://www.wikidata.org/wiki/Q150971","display_name":"Computer graphics","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.26519","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26519","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.2604.26519","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26519","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":{"The":[0],"rapid":[1],"evolution":[2],"of":[3,13,24,133],"deepfake":[4,62],"technology":[5],"poses":[6],"an":[7],"unprecedented":[8],"threat":[9],"to":[10,45,79,96,124,155],"the":[11,55,68,73,103,108,130,145],"authenticity":[12],"Graphics":[14],"Interchange":[15],"Format":[16],"(GIF)":[17],"imagery,":[18],"which":[19,41],"serves":[20],"as":[21],"a":[22,88,116],"representative":[23],"short-loop":[25],"temporal":[26,100],"media":[27],"in":[28,65,158],"social":[29],"networks.":[30],"However,":[31],"existing":[32],"proactive":[33,63,153],"forensics":[34,64,154],"works":[35],"are":[36],"designed":[37],"for":[38,61,129,151],"static":[39],"images,":[40],"limits":[42],"their":[43],"applicability":[44],"animated":[46],"GIFs.":[47,66],"To":[48],"bridge":[49],"this":[50,159],"gap,":[51],"we":[52,71,106,142],"propose":[53,72],"GIFGuard,":[54],"first":[56,146],"spatiotemporal":[57,117],"watermarking":[58],"framework":[59],"tailored":[60],"In":[67,102],"embedding":[69],"stage,":[70,105],"Spatiotemporal":[74],"Adaptive":[75],"Residual":[76],"Encoder":[77],"(STARE)":[78],"ensure":[80],"robustness":[81,172],"against":[82,174],"high-level":[83],"semantic":[84],"tampering.":[85],"It":[86,114],"employs":[87],"3D":[89,122],"convolutional":[90],"backbone":[91],"with":[92,121],"adaptive":[93],"channel":[94],"recalibration":[95],"capture":[97],"globally":[98],"coherent":[99],"dependencies.":[101],"extraction":[104,132],"design":[107],"Deep":[109],"Integrity":[110],"Restoration":[111],"Decoder":[112],"(DIRD).":[113],"utilizes":[115],"hourglass":[118],"architecture":[119],"equipped":[120],"attention":[123],"restore":[125],"latent":[126],"features,":[127],"allowing":[128],"accurate":[131],"watermark":[134],"signals":[135],"even":[136],"under":[137],"severe":[138],"facial":[139],"manipulation.":[140],"Furthermore,":[141],"construct":[143],"GIFfaces,":[144],"large-scale":[147],"benchmark":[148],"dataset":[149,179],"curated":[150],"GIF":[152],"facilitate":[156],"research":[157],"domain.":[160],"Extensive":[161],"results":[162],"show":[163],"that":[164],"GIFGuard":[165],"achieves":[166],"high-fidelity":[167],"visual":[168],"quality":[169],"and":[170,178],"remarkable":[171],"performance":[173],"deepfakes.":[175],"Related":[176],"code":[177],"will":[180],"be":[181],"released.":[182]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-01T00:00:00"}