Face Forgery Detection via Multi‐Scale and Multi‐Domain Features Fusion Article Swipe
Rongrong Gong
,
Jiahao Chen
,
Dengyong Zhang
,
Arun Kumar Sangaiah
,
Mohammed J. F. Alenazi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/ipr2.70131
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/ipr2.70131
Deepfake, as a popular form of visual forgery technique on the Internet, poses a serious threat to individuals' data privacy and security. In consumer electronics, fraudulent schemes leveraging Deepfake technology are widespread, making it urgent to safeguard users' data privacy and security. However, many Deepfake detection methods based on Convolutional Neural Networks (CNNs) struggle to achieve satisfactory performance on mainstream datasets, especially with heavily compressed images. Observing that tampered images leave traces in the frequency domain, which are imperceptible to the naked eye but detectable through spectrum analysis, this study proposes a novel face forgery detection framework integrating spatial and frequency domain features. The framework introduces three innovative modules: the cross‐attention fusion module (CAFM), the guided attention module (GAM), and the multi‐scale feature fusion module (MSFFM), Specifically, CAFM combines spatial and frequency‐domain features through cross‐attention to enhance feature interaction. GAM generates attention maps to refine the integration of spatial and frequency features, while MSFFM fuses multi‐scale hierarchical features to capture both global and local tampering artifacts. These modules collectively improve the richness and discrimination of the extracted features, contributing to the overall detection performance. The proposed method demonstrates its effectiveness and superiority in forgery detection tasks, achieving a 3.9% average improvement in AUC compared to the state‐of‐the‐art method GocNet [1] on FaceForensics++ (FF++) and WildDeepfake datasets. Extensive experiments further validate the effectiveness of our approach.
Related Topics
Concepts
Computer science
Convolutional neural network
Artificial intelligence
Domain (mathematical analysis)
Feature (linguistics)
Pattern recognition (psychology)
Data mining
Face (sociological concept)
Frequency domain
Scale (ratio)
Machine learning
Computer vision
Social science
Mathematical analysis
Philosophy
Quantum mechanics
Physics
Mathematics
Sociology
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/ipr2.70131
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.70131
- OA Status
- gold
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411398064
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4411398064Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1049/ipr2.70131Digital Object Identifier
- Title
-
Face Forgery Detection via Multi‐Scale and Multi‐Domain Features FusionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-01Full publication date if available
- Authors
-
Rongrong Gong, Jiahao Chen, Dengyong Zhang, Arun Kumar Sangaiah, Mohammed J. F. AlenaziList of authors in order
- Landing page
-
https://doi.org/10.1049/ipr2.70131Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.70131Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.70131Direct OA link when available
- Concepts
-
Computer science, Convolutional neural network, Artificial intelligence, Domain (mathematical analysis), Feature (linguistics), Pattern recognition (psychology), Data mining, Face (sociological concept), Frequency domain, Scale (ratio), Machine learning, Computer vision, Social science, Mathematical analysis, Philosophy, Quantum mechanics, Physics, Mathematics, Sociology, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4411398064 |
|---|---|
| doi | https://doi.org/10.1049/ipr2.70131 |
| ids.doi | https://doi.org/10.1049/ipr2.70131 |
| ids.openalex | https://openalex.org/W4411398064 |
| fwci | 0.0 |
| type | article |
| title | Face Forgery Detection via Multi‐Scale and Multi‐Domain Features Fusion |
| biblio.issue | 1 |
| biblio.volume | 19 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12357 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Digital Media Forensic Detection |
| topics[1].id | https://openalex.org/T10775 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9979000091552734 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Generative Adversarial Networks and Image Synthesis |
| topics[2].id | https://openalex.org/T11448 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9977999925613403 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Face recognition and analysis |
| is_xpac | False |
| apc_list.value | 2000 |
| apc_list.currency | EUR |
| apc_list.value_usd | 2200 |
| apc_paid.value | 2000 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2200 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8470370769500732 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C81363708 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6664590239524841 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[1].display_name | Convolutional neural network |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5422277450561523 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C36503486 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5002744197845459 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11235244 |
| concepts[3].display_name | Domain (mathematical analysis) |
| concepts[4].id | https://openalex.org/C2776401178 |
| concepts[4].level | 2 |
| concepts[4].score | 0.46216535568237305 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[4].display_name | Feature (linguistics) |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.46091702580451965 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C124101348 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4280939996242523 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[6].display_name | Data mining |
| concepts[7].id | https://openalex.org/C2779304628 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4267060160636902 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q3503480 |
| concepts[7].display_name | Face (sociological concept) |
| concepts[8].id | https://openalex.org/C19118579 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4177054166793823 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q786423 |
| concepts[8].display_name | Frequency domain |
| concepts[9].id | https://openalex.org/C2778755073 |
| concepts[9].level | 2 |
| concepts[9].score | 0.41214078664779663 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q10858537 |
| concepts[9].display_name | Scale (ratio) |
| concepts[10].id | https://openalex.org/C119857082 |
| concepts[10].level | 1 |
| concepts[10].score | 0.32876574993133545 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[10].display_name | Machine learning |
| concepts[11].id | https://openalex.org/C31972630 |
| concepts[11].level | 1 |
| concepts[11].score | 0.2793753743171692 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[11].display_name | Computer vision |
| concepts[12].id | https://openalex.org/C36289849 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q34749 |
| concepts[12].display_name | Social science |
| concepts[13].id | https://openalex.org/C134306372 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[13].display_name | Mathematical analysis |
| concepts[14].id | https://openalex.org/C138885662 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[14].display_name | Philosophy |
| concepts[15].id | https://openalex.org/C62520636 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[15].display_name | Quantum mechanics |
| concepts[16].id | https://openalex.org/C121332964 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[16].display_name | Physics |
| concepts[17].id | https://openalex.org/C33923547 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[17].display_name | Mathematics |
| concepts[18].id | https://openalex.org/C144024400 |
| concepts[18].level | 0 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q21201 |
| concepts[18].display_name | Sociology |
| concepts[19].id | https://openalex.org/C41895202 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[19].display_name | Linguistics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8470370769500732 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[1].score | 0.6664590239524841 |
| keywords[1].display_name | Convolutional neural network |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.5422277450561523 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/domain |
| keywords[3].score | 0.5002744197845459 |
| keywords[3].display_name | Domain (mathematical analysis) |
| keywords[4].id | https://openalex.org/keywords/feature |
| keywords[4].score | 0.46216535568237305 |
| keywords[4].display_name | Feature (linguistics) |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.46091702580451965 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/data-mining |
| keywords[6].score | 0.4280939996242523 |
| keywords[6].display_name | Data mining |
| keywords[7].id | https://openalex.org/keywords/face |
| keywords[7].score | 0.4267060160636902 |
| keywords[7].display_name | Face (sociological concept) |
| keywords[8].id | https://openalex.org/keywords/frequency-domain |
| keywords[8].score | 0.4177054166793823 |
| keywords[8].display_name | Frequency domain |
| keywords[9].id | https://openalex.org/keywords/scale |
| keywords[9].score | 0.41214078664779663 |
| keywords[9].display_name | Scale (ratio) |
| keywords[10].id | https://openalex.org/keywords/machine-learning |
| keywords[10].score | 0.32876574993133545 |
| keywords[10].display_name | Machine learning |
| keywords[11].id | https://openalex.org/keywords/computer-vision |
| keywords[11].score | 0.2793753743171692 |
| keywords[11].display_name | Computer vision |
| language | en |
| locations[0].id | doi:10.1049/ipr2.70131 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S83215360 |
| locations[0].source.issn | 1751-9659, 1751-9667 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1751-9659 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | IET Image Processing |
| locations[0].source.host_organization | https://openalex.org/P4310311714 |
| locations[0].source.host_organization_name | Institution of Engineering and Technology |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310311714 |
| locations[0].source.host_organization_lineage_names | Institution of Engineering and Technology |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.70131 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | IET Image Processing |
| locations[0].landing_page_url | https://doi.org/10.1049/ipr2.70131 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5114036552 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Rongrong Gong |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I198357462 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Software Changsha Social Work College Changsha China |
| authorships[0].institutions[0].id | https://openalex.org/I198357462 |
| authorships[0].institutions[0].ror | https://ror.org/011d8sm39 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I198357462 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Changsha University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Rongrong Gong |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Software Changsha Social Work College Changsha China |
| authorships[1].author.id | https://openalex.org/A5107772117 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7961-1944 |
| authorships[1].author.display_name | Jiahao Chen |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I56934997 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Computer Science and Technology Changsha University of Science and Technology Changsha China |
| authorships[1].institutions[0].id | https://openalex.org/I56934997 |
| authorships[1].institutions[0].ror | https://ror.org/03yph8055 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I56934997 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Changsha University of Science and Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jiahao Chen |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Computer Science and Technology Changsha University of Science and Technology Changsha China |
| authorships[2].author.id | https://openalex.org/A5085673014 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2789-2980 |
| authorships[2].author.display_name | Dengyong Zhang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I56934997 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Computer Science and Technology Changsha University of Science and Technology Changsha China |
| authorships[2].institutions[0].id | https://openalex.org/I56934997 |
| authorships[2].institutions[0].ror | https://ror.org/03yph8055 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I56934997 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Changsha University of Science and Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Dengyong Zhang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Computer Science and Technology Changsha University of Science and Technology Changsha China |
| authorships[3].author.id | https://openalex.org/A5072241102 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-0229-2460 |
| authorships[3].author.display_name | Arun Kumar Sangaiah |
| authorships[3].countries | TW |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I75357094 |
| authorships[3].affiliations[0].raw_affiliation_string | International Graduate Institute of AI National Yunlin University of Science and Technology Douliou Taiwan |
| authorships[3].institutions[0].id | https://openalex.org/I75357094 |
| authorships[3].institutions[0].ror | https://ror.org/04qkq2m54 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I75357094 |
| authorships[3].institutions[0].country_code | TW |
| authorships[3].institutions[0].display_name | National Yunlin University of Science and Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Arun Kumar Sangaiah |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | International Graduate Institute of AI National Yunlin University of Science and Technology Douliou Taiwan |
| authorships[4].author.id | https://openalex.org/A5075968009 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6593-112X |
| authorships[4].author.display_name | Mohammed J. F. Alenazi |
| authorships[4].countries | SA |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I28022161 |
| authorships[4].affiliations[0].raw_affiliation_string | Computer Engineering Department, College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia |
| authorships[4].institutions[0].id | https://openalex.org/I28022161 |
| authorships[4].institutions[0].ror | https://ror.org/02f81g417 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I28022161 |
| authorships[4].institutions[0].country_code | SA |
| authorships[4].institutions[0].display_name | King Saud University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Mohammed J. F. Alenazi |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Computer Engineering Department, College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.70131 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Face Forgery Detection via Multi‐Scale and Multi‐Domain Features Fusion |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12357 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Digital Media Forensic Detection |
| related_works | https://openalex.org/W4391621807, https://openalex.org/W4321487865, https://openalex.org/W4313906399, https://openalex.org/W4391621790, https://openalex.org/W4239306820, https://openalex.org/W4391266461, https://openalex.org/W2590798552, https://openalex.org/W2811106690, https://openalex.org/W2947043951, https://openalex.org/W2132337154 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1049/ipr2.70131 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S83215360 |
| best_oa_location.source.issn | 1751-9659, 1751-9667 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1751-9659 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | IET Image Processing |
| best_oa_location.source.host_organization | https://openalex.org/P4310311714 |
| best_oa_location.source.host_organization_name | Institution of Engineering and Technology |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311714 |
| best_oa_location.source.host_organization_lineage_names | Institution of Engineering and Technology |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.70131 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | IET Image Processing |
| best_oa_location.landing_page_url | https://doi.org/10.1049/ipr2.70131 |
| primary_location.id | doi:10.1049/ipr2.70131 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S83215360 |
| primary_location.source.issn | 1751-9659, 1751-9667 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1751-9659 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | IET Image Processing |
| primary_location.source.host_organization | https://openalex.org/P4310311714 |
| primary_location.source.host_organization_name | Institution of Engineering and Technology |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311714 |
| primary_location.source.host_organization_lineage_names | Institution of Engineering and Technology |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.70131 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IET Image Processing |
| primary_location.landing_page_url | https://doi.org/10.1049/ipr2.70131 |
| publication_date | 2025-01-01 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4310017951, https://openalex.org/W2603123944, https://openalex.org/W2531409750, https://openalex.org/W2963446712, https://openalex.org/W2946948417, https://openalex.org/W4297095254, https://openalex.org/W4381302520, https://openalex.org/W4312782746, https://openalex.org/W4221091880, https://openalex.org/W3183999072, https://openalex.org/W3094728142, https://openalex.org/W2997517962, https://openalex.org/W3175342695, https://openalex.org/W2913399670, https://openalex.org/W3017837134, https://openalex.org/W3092997524, https://openalex.org/W2963684180, https://openalex.org/W2891145043, https://openalex.org/W2911424785, https://openalex.org/W2963720850, https://openalex.org/W2945262873, https://openalex.org/W3188946793, https://openalex.org/W3174656926, https://openalex.org/W3015341856, https://openalex.org/W4321100337, https://openalex.org/W2964146055, https://openalex.org/W2982058372, https://openalex.org/W2301937176, https://openalex.org/W2942074357, https://openalex.org/W2980459401, https://openalex.org/W3034713808, https://openalex.org/W3092879151, https://openalex.org/W3034795015, https://openalex.org/W3154326567, https://openalex.org/W3155334261, https://openalex.org/W3034196597, https://openalex.org/W2962858109 |
| referenced_works_count | 37 |
| abstract_inverted_index.a | 3, 14, 92, 198 |
| abstract_inverted_index.In | 23 |
| abstract_inverted_index.as | 2 |
| abstract_inverted_index.in | 73, 193, 202 |
| abstract_inverted_index.it | 34 |
| abstract_inverted_index.of | 6, 148, 175, 223 |
| abstract_inverted_index.on | 10, 49, 59, 211 |
| abstract_inverted_index.to | 17, 36, 55, 80, 136, 144, 159, 180, 205 |
| abstract_inverted_index.AUC | 203 |
| abstract_inverted_index.GAM | 140 |
| abstract_inverted_index.The | 104, 185 |
| abstract_inverted_index.[1] | 210 |
| abstract_inverted_index.and | 21, 41, 100, 120, 131, 150, 163, 173, 191, 214 |
| abstract_inverted_index.are | 31, 78 |
| abstract_inverted_index.but | 84 |
| abstract_inverted_index.eye | 83 |
| abstract_inverted_index.its | 189 |
| abstract_inverted_index.our | 224 |
| abstract_inverted_index.the | 11, 74, 81, 110, 115, 121, 146, 171, 176, 181, 206, 221 |
| abstract_inverted_index.3.9% | 199 |
| abstract_inverted_index.CAFM | 128 |
| abstract_inverted_index.both | 161 |
| abstract_inverted_index.data | 19, 39 |
| abstract_inverted_index.face | 94 |
| abstract_inverted_index.form | 5 |
| abstract_inverted_index.many | 44 |
| abstract_inverted_index.maps | 143 |
| abstract_inverted_index.that | 68 |
| abstract_inverted_index.this | 89 |
| abstract_inverted_index.with | 63 |
| abstract_inverted_index.MSFFM | 154 |
| abstract_inverted_index.These | 167 |
| abstract_inverted_index.based | 48 |
| abstract_inverted_index.fuses | 155 |
| abstract_inverted_index.leave | 71 |
| abstract_inverted_index.local | 164 |
| abstract_inverted_index.naked | 82 |
| abstract_inverted_index.novel | 93 |
| abstract_inverted_index.poses | 13 |
| abstract_inverted_index.study | 90 |
| abstract_inverted_index.three | 107 |
| abstract_inverted_index.which | 77 |
| abstract_inverted_index.while | 153 |
| abstract_inverted_index.(CNNs) | 53 |
| abstract_inverted_index.(FF++) | 213 |
| abstract_inverted_index.(GAM), | 119 |
| abstract_inverted_index.GocNet | 209 |
| abstract_inverted_index.Neural | 51 |
| abstract_inverted_index.domain | 102 |
| abstract_inverted_index.fusion | 112, 124 |
| abstract_inverted_index.global | 162 |
| abstract_inverted_index.guided | 116 |
| abstract_inverted_index.images | 70 |
| abstract_inverted_index.making | 33 |
| abstract_inverted_index.method | 187, 208 |
| abstract_inverted_index.module | 113, 118, 125 |
| abstract_inverted_index.refine | 145 |
| abstract_inverted_index.tasks, | 196 |
| abstract_inverted_index.threat | 16 |
| abstract_inverted_index.traces | 72 |
| abstract_inverted_index.urgent | 35 |
| abstract_inverted_index.users' | 38 |
| abstract_inverted_index.visual | 7 |
| abstract_inverted_index.(CAFM), | 114 |
| abstract_inverted_index.achieve | 56 |
| abstract_inverted_index.average | 200 |
| abstract_inverted_index.capture | 160 |
| abstract_inverted_index.domain, | 76 |
| abstract_inverted_index.enhance | 137 |
| abstract_inverted_index.feature | 123, 138 |
| abstract_inverted_index.forgery | 8, 95, 194 |
| abstract_inverted_index.further | 219 |
| abstract_inverted_index.heavily | 64 |
| abstract_inverted_index.images. | 66 |
| abstract_inverted_index.improve | 170 |
| abstract_inverted_index.methods | 47 |
| abstract_inverted_index.modules | 168 |
| abstract_inverted_index.overall | 182 |
| abstract_inverted_index.popular | 4 |
| abstract_inverted_index.privacy | 20, 40 |
| abstract_inverted_index.schemes | 27 |
| abstract_inverted_index.serious | 15 |
| abstract_inverted_index.spatial | 99, 130, 149 |
| abstract_inverted_index.through | 86, 134 |
| abstract_inverted_index.(MSFFM), | 126 |
| abstract_inverted_index.ABSTRACT | 0 |
| abstract_inverted_index.Deepfake | 29, 45 |
| abstract_inverted_index.However, | 43 |
| abstract_inverted_index.Networks | 52 |
| abstract_inverted_index.combines | 129 |
| abstract_inverted_index.compared | 204 |
| abstract_inverted_index.consumer | 24 |
| abstract_inverted_index.features | 133, 158 |
| abstract_inverted_index.modules: | 109 |
| abstract_inverted_index.proposed | 186 |
| abstract_inverted_index.proposes | 91 |
| abstract_inverted_index.richness | 172 |
| abstract_inverted_index.spectrum | 87 |
| abstract_inverted_index.struggle | 54 |
| abstract_inverted_index.tampered | 69 |
| abstract_inverted_index.validate | 220 |
| abstract_inverted_index.Deepfake, | 1 |
| abstract_inverted_index.Extensive | 217 |
| abstract_inverted_index.Internet, | 12 |
| abstract_inverted_index.Observing | 67 |
| abstract_inverted_index.achieving | 197 |
| abstract_inverted_index.analysis, | 88 |
| abstract_inverted_index.approach. | 225 |
| abstract_inverted_index.attention | 117, 142 |
| abstract_inverted_index.datasets, | 61 |
| abstract_inverted_index.datasets. | 216 |
| abstract_inverted_index.detection | 46, 96, 183, 195 |
| abstract_inverted_index.extracted | 177 |
| abstract_inverted_index.features, | 152, 178 |
| abstract_inverted_index.features. | 103 |
| abstract_inverted_index.framework | 97, 105 |
| abstract_inverted_index.frequency | 75, 101, 151 |
| abstract_inverted_index.generates | 141 |
| abstract_inverted_index.safeguard | 37 |
| abstract_inverted_index.security. | 22, 42 |
| abstract_inverted_index.tampering | 165 |
| abstract_inverted_index.technique | 9 |
| abstract_inverted_index.artifacts. | 166 |
| abstract_inverted_index.compressed | 65 |
| abstract_inverted_index.detectable | 85 |
| abstract_inverted_index.especially | 62 |
| abstract_inverted_index.fraudulent | 26 |
| abstract_inverted_index.innovative | 108 |
| abstract_inverted_index.introduces | 106 |
| abstract_inverted_index.leveraging | 28 |
| abstract_inverted_index.mainstream | 60 |
| abstract_inverted_index.technology | 30 |
| abstract_inverted_index.experiments | 218 |
| abstract_inverted_index.improvement | 201 |
| abstract_inverted_index.integrating | 98 |
| abstract_inverted_index.integration | 147 |
| abstract_inverted_index.performance | 58 |
| abstract_inverted_index.superiority | 192 |
| abstract_inverted_index.widespread, | 32 |
| abstract_inverted_index.WildDeepfake | 215 |
| abstract_inverted_index.collectively | 169 |
| abstract_inverted_index.contributing | 179 |
| abstract_inverted_index.demonstrates | 188 |
| abstract_inverted_index.electronics, | 25 |
| abstract_inverted_index.hierarchical | 157 |
| abstract_inverted_index.individuals' | 18 |
| abstract_inverted_index.interaction. | 139 |
| abstract_inverted_index.performance. | 184 |
| abstract_inverted_index.satisfactory | 57 |
| abstract_inverted_index.Convolutional | 50 |
| abstract_inverted_index.Specifically, | 127 |
| abstract_inverted_index.effectiveness | 190, 222 |
| abstract_inverted_index.imperceptible | 79 |
| abstract_inverted_index.multi‐scale | 122, 156 |
| abstract_inverted_index.discrimination | 174 |
| abstract_inverted_index.FaceForensics++ | 212 |
| abstract_inverted_index.cross‐attention | 111, 135 |
| abstract_inverted_index.frequency‐domain | 132 |
| abstract_inverted_index.state‐of‐the‐art | 207 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| sustainable_development_goals[1].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[1].score | 0.4099999964237213 |
| sustainable_development_goals[1].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.22625137 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |