The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.
MFC Datasets: Large-Scale Benchmark Datasets for Media Forensic Challenge Evaluation
Abstract
We provide a benchmark for digital Media Forensics Challenge (MFC) evaluations. Our comprehensive data comprises over 176,000 high provenance (HP) images and 11,000 HP videos; more than 100,000 manipulated images and 4,000 manipulated videos; 35 million internet images and 300,000 video clips. We have designed and generated a series of development, evaluation, and challenge datasets, and used them to assess the progress and thoroughly analyze the performance of diverse systems on a variety of media forensics tasks in the past two years. In this paper, we first introduce the objectives, challenges, and approaches to building media forensics evaluation datasets. We then discuss our approaches to forensic dataset collection, annotation, and manipulation, and present the design and infrastructure to effectively and efficiently build the evaluation datasets to support various evaluation tasks. Given a specified query, we build an infrastructure that selects the customized evaluation subsets for the targeted analysis report. Finally, we demonstrate the evaluation results in the past evaluations.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Test results show that the popular face recog- nition systems based on VGG [65] and Facenet [59, 66] are unable to detect deepfakes effectively.”
From Deep learning for deepfakes creation and detection: A survey (2022) · cites this paper as [59]
Citations
Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017) · arXiv · doi:10.48550/arxiv.1710.10196 · Cited by 4,727
SUN database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539970 · Cited by 3,283
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
Torralba, A., Fergus, R., & Freeman, W. T. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.128 · Cited by 1,921
Thomée, B., Shamma, D. A., Friedland, G. H., Elizalde, B., Ni, K. S., Poland, D., Borth, D., & Li, L.-J. (2016) · Communications of the ACM · doi:10.1145/2812802 · Cited by 1,715
Evaluation campaigns and TRECVid
Smeaton, A. F., Over, P., & Kraaij, W. (2006) · doi:10.1145/1178677.1178722 · Cited by 1,227
UCID: an uncompressed color image database
Schaefer, G., & Stich, M. (2003) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.525375 · Cited by 1,078
Research works printed but not linked: 17
- rossler, 2018, FaceForensics A Large-scale Video Dataset for Forgery Detection in Human Faces
- dong, 2013, CASIA image tampering detection evaluation database, in Signal and Information Processing (ChinaSIP), IEEE China Summit & International Conference on, 422
- 2015, A Database of Photos of Plants Animals Habitats and Other Natural History Subjects
- bansal, 2016, Umdfaces An annotated face dataset for training deep networks
- bas, 2011, Break Our Steganographic System, The Ins and Outs of Organizing BOSS INFORMATION HIDING Czech Republic 6958/2011 59-70 Lecture Notes in Computer Science
- 0, DARPA MediFor
- yates, 2017, Nimble Challenge 2017 Evaluation Plan, NIST
- yates, 2018, Media Forensics Challenge 2018 Evaluation Plan, NIST
- griffin, 2007, Caltech-256 Object Category Dataset California Institute of Technology
- 0, EU project REWIND (REVerse engineering of audio-VIsual coNtent Data)
- rocha, 0, The First IFS-TC Image Forensics Challenge
- boehme, 2012, Counter-Forensics Attacking Image Forensics Digital Image Forensics, 327
- wang, 2018, Video-to-Video Synthesis
- lyu, 2012, Natural Image Statistics in Digital Image Forensics, Digital Image Forensics, 239
- boylan, 2018, Will Deep-Fake Technology Destroy Democracy?
- robertson, 2018, Manual for MaskGen Journaling Tool
- schellmann, 2018, Deepfake Videos Are Getting Real and That's a Problem
Cited by
Sorted by citations
Items 101–200 of 334
Image splicing localization using residual image and residual-based fully convolutional network
Chen, B., Qi, X., Zhou, Y., Yang, G., Zheng, Y., & Xiao, B. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.102967 · Cited by 18
Deepfake detection: critical review of state-of-the-art approaches and future perspectives
Soundarya, B. C., & Gururaj, H. L. (2026) · Discover Applied Sciences · doi:10.1007/s42452-025-08174-9 · Cited by 18
Xu, X., Lv, W., Wang, W. W., Zhang, Y., & Chen, J. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3347178 · Cited by 17
Image Manipulation Detection With Cascade Hierarchical Graph Representation
Pan, W., Ma, W., Zhao, S., Gu, L., Shi, G., Xia, Z., & Wang, M. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3390127 · Cited by 17
Multi-scale noise-guided progressive network for image splicing detection and localization
Zhang, D., Jiang, N., Li, F., Chen, J., Liao, X., Yang, G., & Ding, X. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124975 · Cited by 19
A Lightweight and Effective Image Tampering Localization Network With Vision Mamba
Guo, K., Cao, G., Lou, Z., Huang, X., & Liu, J. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3570240 · Cited by 19
Li, F., Pei, Z., Wei, W., Li, J., & Qin, C. (2022) · Security and Communication Networks · doi:10.1155/2022/1090307 · Cited by 17
Peng, R., Tan, S., Mo, X., Li, B., & Huang, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3381470 · Cited by 17
TGIF: Text-Guided Inpainting Forgery Dataset
Mareen, H., Karageorgiou, D., Wallendael, G. V., Lambert, P., & Papadopoulos, S. (2024) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs61860.2024.10810690 · Cited by 25
Image Forgery Localization With State Space Models
Lou, Z., Cao, G., Guo, K., Weng, S., & Yu, L. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3559429 · Cited by 16
Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection
Fezza, S. A., Ouis, M. Y., Kaddar, B., Hamidouche, W., & Hadid, A. (2022) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp55362.2022.9949282 · Cited by 17
Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis
Mittal, T., Sinha, R., Swaminathan, V., Collomosse, J. P., & Manocha, D. (2023) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw58289.2023.00071 · Cited by 17
On the effectiveness of handcrafted features for deepfake video detection
Kaddar, B., Fezza, S. A., Hamidouche, W., Akhtar, Z., Hadid, A., Serra-Sagrista, J. S., Hernández-Cabronero, M., & Sanchez, V. (2023) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.5.053033 · Cited by 15
Benchmarking Scientific Image Forgery Detectors
Cardenuto, J. P., & Rocha, A. D. R. (2022) · Science and Engineering Ethics · doi:10.1007/s11948-022-00391-4 · Cited by 14
Zhou, Y., Wang, H., Zeng, Q., Zhang, R., & Meng, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123501 · Cited by 14
MSF-Net: Multi-stream fusion network for image manipulation detection and localization
Dou, L., Chen, M., Qiu, J., & Wang, J. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105114 · Cited by 14
A Contribution-Aware Noise Feature representation model for image manipulation localization
Zhou, Y., Wang, H., Zeng, Q., Zhang, R., & Meng, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111988 · Cited by 13
Exposing image splicing traces in scientific publications via uncertainty-guided refinement
Lin, X., Tang, W., Wang, H., Liu, Y., Ju, Y., Wang, S., & Yu, Z. (2024) · Patterns · doi:10.1016/j.patter.2024.101038 · Cited by 15
Robustifying vision transformer for image forgery localization with multi-exit architectures
Shi, Z., Chen, H., & Zhang, D. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111565 · Cited by 14
Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection
Wang, J., Li, Z., Zhang, C., Chen, J., Wu, Z., Davis, L. S., & Jiang, Y. (2025) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3576367 · Cited by 18
Multi-Task Wavelet Corrected Network for Image Splicing Forgery Detection and Localization
Bi, X., Zhang, Z., Liu, Y., Xiao, B., & Li, W. (2021) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428466 · Cited by 13
CatmullRom Splines-Based Regression for Image Forgery Localization
Zhang, L., Xu, M., Li, D., Du, J., & Wang, R. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i7.28548 · Cited by 13
BioFors: A Large Biomedical Image Forensics Dataset
Sabir, E., Nandi, S., AbdAlmageed, W., & Natarajan, P. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01078 · Cited by 12
“BioFors is a reasonably large dataset at the intersection of biomedical and image-forensics domain, with 46,064 pristine and 1,741 manipulated images, when compared to biomedical image datasets including FMD [55] (12,000 images before augmentation) and CVPPP [42] (284 images) and also compared to image forensics datas…”
BackgroundFeature Aggregation and Region-Aware Learning for Detection of Splicing Forgery
Xu, Y., Zheng, J., Ren, J., & Fang, A. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3348689 · Cited by 11
Miao, C., Chu, Q., Gong, T., Tan, Z., Jin, Z., Zhuang, W., Luo, M., Hu, H., & Yu, N. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755231 · Cited by 20
Image Forgery Localization Using U-Net based Architecture and Error Level Analysis
Hebbar, N. K., & Kunte, A. (2021) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n53548.2021.9725373 · Cited by 11
A Versatile Detection Method for Various Contrast Enhancement Manipulations
Bi, X., Shang, Y., Liu, B., Xiao, B., Li, W., & Gao, X. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3204789 · Cited by 11
A comprehensive analysis of digital video forensics techniques and challenges
Sandhya, & Kashyap, A. (2023) · Iran Journal of Computer Science · doi:10.1007/s42044-023-00165-6 · Cited by 11
Unsupervised Image Manipulation Localization With Non-Binary Label Attribution
Hosseini, M. D. M., & Kirchner, M. (2019) · IEEE Signal Processing Letters · doi:10.1109/lsp.2019.2913530 · Cited by 11
Harmonizing Image Forgery Detection & Localization: Fusion of Complementary Approaches
Mareen, H., Neve, L. D., Lambert, P., & Wallendael, G. V. (2023) · Journal of Imaging · doi:10.3390/jimaging10010004 · Cited by 10
Moreira, D., Theisen, W., Scheirer, W. J., Bharati, A., Brogan, J., & Rocha, A. D. R. (2022) · Advances in computer vision and pattern recognition · doi:10.1007/978-981-16-7621-5_15 · Cited by 10
Dual-Stream Intermediate Fusion Network for Image Forgery Localization
Yan, C., Liu, R., Li, H., Wu, J., & Pan, H. (2024) · IEEE Access · doi:10.1109/access.2024.3413709 · Cited by 9
Noise-Assisted Prompt Learning for Image Forgery Detection and Localization
Li, D., Zhu, J., Fu, X., Guo, X., Liu, Y., Yang, G., Liu, J., & Zha, Z.-J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73247-8_2 · Cited by 12
DFST-UNet: Dual-Domain Fusion Swin Transformer U-Net for Image Forgery Localization
Yang, J., Xie, A., Mai, T., & Chen, Y. (2025) · Entropy · doi:10.3390/e27050535 · Cited by 9
A New Dataset for Forged Smartphone Videos Detection: Description and Analysis
Akbari, Y., Najeeb, A. A., Al-Maadeed, S. A., Elharrouss, O., Khelifi, F., & Lawgaly, A. (2023) · IEEE Access · doi:10.1109/access.2023.3267743 · Cited by 9
Deepfakes in Cyber Warfare: Threats, Detection, Techniques and Countermeasures
Cinar, B. (2023) · Asian Journal of Research in Computer Science · doi:10.9734/ajrcos/2023/v16i4381 · Cited by 10
Shallow- and Deep- fake Image Manipulation Localization Using Deep Learning
Zhang, J., Tohidypour, H., Wang, Y., & Nasiopoulos, P. (2023) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/icnc57223.2023.10074246 · Cited by 11
Li, F., Zhai, H., Zhang, X., & Qin, C. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3338703 · Cited by 9
Chinese Perspectives on AI and Future Military Capabilities
Fedasiuk, R. (2020) · doi:10.51593/20200022 · Cited by 9
User guide for NIST Media Forensic Challenge (MFC) datasets
Guan, H., Delgado, A., Lee, Y., Yates, A. N., Zhou, D., Kheyrkhah, T., & Fiscus, J. (2021) · doi:10.6028/nist.ir.8377 · Cited by 8
Self-citationRethinking Image Editing Detection in the Era of Generative AI Revolution
Sun, Z., Fang, H., Cao, J., Zhao, X., & Wang, D. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681445 · Cited by 11
Dual-decoding branch contrastive augmentation for image manipulation localization
Zeng, Q., Wang, H., Zhou, Y., & Zhang, R. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112776 · Cited by 8
Wang, H., Cheng, H., Chen, Y., Xu, Y., & Wang, M. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130255 · Cited by 8
Deep Learning for Multimedia Forensics
Amerini, I., Anagnostopoulos, A., Maiano, L., & Celsi, L. R. (2021) · doi:10.1561/9781680838558 · Cited by 8
Semi-supervised image manipulation localization with residual enhancement
Zeng, Q., Wang, H., Zhou, Y., Zhang, R., & Meng, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124171 · Cited by 7
Multi-view Feature Extraction via Tunable Prompts is Enough for Image Manipulation Localization
Liu, X., Yang, Y., Wang, H., Ying, Q., Qian, Z., Zhang, X., & Li, S. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681137 · Cited by 8
CMID: A New Dataset for Copy-Move Forgeries on ID Documents
Mahfoudi, G., Morain-Nicolier, F., Retraint, F., & Pic, M. M. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506723 · Cited by 7
AbhAS: A Novel Realistic Image Splicing Forensics Dataset
Gokhale, A., Thepade, S. D., Aarons, N. R., Pramod, D., & Kulkarni, R. (2020) · Journal of Applied Security Research · doi:10.1080/19361610.2020.1811059 · Cited by 7
Zhou, Y., Wang, H., Zeng, Q., Zhang, R., & Meng, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096501 · Cited by 7
AdaIFL: Adaptive Image Forgery Localization via a Dynamic and Importance-Aware Transformer Network
Li, Y., Cheng, F., Yu, W., Wang, G., Luo, G., & Zhu, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72775-7_27 · Cited by 6
Deep Learning Based Image Forgery Detection Methods
Xiu-jian, L., & Sun, H. (2022) · Journal of Cyber Security · doi:10.32604/jcs.2022.032915 · Cited by 7
Interpol review of forensic video analysis, 2019–2022
Geradts, Z., & Riphagen, Q. (2023) · Forensic Science International Synergy · doi:10.1016/j.fsisyn.2022.100309 · Cited by 9
PL-GNet: Pixel Level Global Network for detection and localization of image forgeries
Shi, Z., Shen, X., Chen, H., & Lyu, Y. (2023) · Signal Processing Image Communication · doi:10.1016/j.image.2023.117029 · Cited by 6
DSSE-net: dual stream skip edge-enhanced network with forgery loss for image forgery localization
Zheng, A., Huang, T., Huang, W., Huang, L., Ye, F., & Luo, H. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02031-0 · Cited by 6
Learning Compressed Artifact for JPEG Manipulation Localization Using Wide-Receptive-Field Network
Li, F., Zhai, H., Liu, T., Zhang, X., & Qin, C. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3678883 · Cited by 6
MSU-Net: the multi-scale supervised U-Net for image splicing forgery localization
Yu, H., Su, L., Dai, C., & Wang, J. (2024) · Pattern Analysis and Applications · doi:10.1007/s10044-024-01305-9 · Cited by 6
Auto-focus tracing: Image manipulation detection with artifact graph contrastive
Pan, W., Xia, Z., Ma, W., Wang, Y., Gu, L., Shi, G., & Zhao, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112545 · Cited by 6
Bai, R. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113033 · Cited by 6
Shao, Y., Wang, T., & Wang, L. (2025) · The European Journal on Artificial Intelligence · doi:10.1177/30504554241301395 · Cited by 6
Jin, X., Lee, Y., Fiscus, J. G., Guan, H., Yates, A. N., Delgado, A., & Zhou, D. F. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.10.042 · Cited by 6
Self-citationData-Driven Digital Integrity Verification
Cozzolino, D., Poggi, G., & Verdoliva, L. (2022) · Advances in computer vision and pattern recognition · doi:10.1007/978-981-16-7621-5_11 · Cited by 6
CDS-Net: Cooperative dual-stream network for image manipulation detection
Wang, H., Deng, J., Lin, X., Wang, S., & Wang, S. (2023) · Pattern Recognition Letters · doi:10.1016/j.patrec.2023.11.005 · Cited by 6
Video Frame Deletion and Duplication
Long, C., Basharat, A., & Hoogs, A. J. (2022) · Advances in computer vision and pattern recognition · doi:10.1007/978-981-16-7621-5_13 · Cited by 6
Scale-Adaptive Deep Matching Network for Constrained Image Splicing Detection and Localization
Xu, S., Lv, S., Liu, Y., Xia, C. Y., & Gan, N. (2022) · Applied Sciences · doi:10.3390/app12136480 · Cited by 6
Zeng, Y., & Pun, C. (2024) · The Visual Computer · doi:10.1007/s00371-024-03482-4 · Cited by 5
Tian, X., Zhao, J., & Wen, L. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03389-6 · Cited by 5
Thakur, R., & Rohilla, R. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04407-x · Cited by 5
ICDAR 2023 Competition on Detecting Tampered Text in Images
Luo, D., Zhou, Y., Yang, R., Liu, Y., Liu, X., Zeng, J., Zhang, E., Yang, B., Huang, Z., Jin, L., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-41679-8_36 · Cited by 5
Image tampering localization network based on multi-class attention and progressive subtraction
Shao, Y., Dai, K., & Wang, L. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03622-2 · Cited by 5
2018 Media Forensics Challenges (MFC18) :
Lee, Y., Yates, A. N., Guan, H., Delgado, A., Zhou, D. F., Kheyrkhah, T., & Fiscus, J. G. (2020) · doi:10.6028/nist.ir.8324 · Cited by 5
Self-citationOmni-IML: Towards Unified Image Manipulation LocalizationPreprint
Qu, C., Zhong, Y., Guo, F., & Jin, L. (2024) · Qeios · doi:10.32388/mnreu8 · Cited by 14
Diff-IFL: Towards General Image Forgery Localization using Diffusion Probabilistic Model
Xu, P., Ni, J., Zhang, J., Zhang, Y., & Tang, S. (2024) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme57554.2024.10687635 · Cited by 5
BASNet: Boundary Assisted Network for Image Splicing Forgery Detection
Liang, E., Zhang, K., Hua, Z., & Jia, X. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3604911 · Cited by 4
TextSleuth: A New Dataset and Baseline for Scene Text Manipulation Detection
Pandey, A. K., Chang, M., & Li, X. (2024) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr62202.2024.00064 · Cited by 4
Chen, Y., Chang, M., Kirchner, M., Zhang, Z., Li, X., Basharat, A., & Hoogs, A. J. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00744 · Cited by 6
Zhu, J., Dong, L., Fu, X., Shi, G., Xiao, J., Liu, A., & Zha, Z.-J. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i10.33192 · Cited by 3
Zhang, H., Zeng, J., & Yang, J. (2025) · Electronics · doi:10.3390/electronics14091877 · Cited by 5
A multi-scale feature and cross-domain fusion network for image tampering localization
Deng, F., Liu, T., Yu, H., & Yang, R. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111325 · Cited by 4
AGU2-Net: Multi-Scale U 2 -Net Enhanced by Attention Gate Mechanism for Image Tampering Localization
Wu, K.-F., Li, L., & Li, Q. (2025) · IEEE Access · doi:10.1109/access.2025.3577221 · Cited by 4
Dual-Branch Noise-Guided Network for Image Splicing Forgery Detection
Liang, E., Zhang, K., Hua, Z., & Jia, X. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3589929 · Cited by 4
Xu, P., Ni, J., Nie, F., & Zhang, J. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3661555 · Cited by 4
Tan, X., Zhang, H., Wang, Z., & Tang, J. (2023) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.5.053012 · Cited by 4
Detection of Image Tampering Using Multiscale Fusion and Anomalousness Assessment
Wang, Y., Liu, L., & Huang, T. (2024) · Advances in transdisciplinary engineering · doi:10.3233/atde240476 · Cited by 4
Multi-Scale Feature Attention Fusion for Image Splicing Forgery Detection
Liang, E., Zhang, K., Hua, Z., & Jia, X. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3698770 · Cited by 4
Cheng, H., Niu, L., Zhang, Z., & Ye, L. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104396 · Cited by 4
Gan, Z., Han, W., Xie, Z., Zhang, B., & Chai, X. (2025) · Multimedia Systems · doi:10.1007/s00530-025-01874-7 · Cited by 4
Three-stage image forgery localization with shallow feature enhancement and attention
Chai, X., Song, S., Tan, Y. S., Yan, L., Huang, Z., Yang, L., & Wei, T. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.6.063051 · Cited by 4
A Deep Learning Framework with Transfer Learning Approach for Image Forgery Localization
Hebbar, N. K., & Kunte, A. (2022) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat55367.2022.9971986 · Cited by 4
MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks
Xiang, Z., Yadav, A. K. S., Bestagini, P., Tubaro, S., & Delp, E. J. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00103 · Cited by 4
A Parallel Attention Mechanism for Image Manipulation Detection and Localization
Zeng, Q., Wang, H., Zhou, Y., Zhang, R., & Meng, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097225 · Cited by 4
Learning Interpretable Forensic Representations via Local Window Modulation
Das, S., & Amin, M. R. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00050 · Cited by 4
Open Media Forensics Challenge (OpenMFC) 2020-2021 :
Guan, H., Lee, Y., Diduch, L. L., Zhang, J. G., Ghorbanian, I., Kheyrkhah, T., Fontana, P., Fiscus, J., & Filliben, J. J. (2021) · doi:10.6028/nist.ir.8396 · Cited by 4
Self-citationImage Provenance Analysis via Graph Encoding With Vision Transformer
Zhang, K., Kong, C., Wang, S., Rocha, A. D. R., & Li, H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3559787 · Cited by 5
Decoding fake news fabrications and trends: A comprehensive survey
Kumar, G., & Dhiman, C. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131118 · Cited by 3
AMSEANet: An Edge-Guided Adaptive Multi-Scale Network for Image Splicing Detection and Localization
Yang, Y., He, Y., Ma, X., Lv, W., Chen, J., & Wang, H. (2025) · Sensors · doi:10.3390/s25206494 · Cited by 3
A Novel Universal Image Forensics Localization Model Based on Image Noise and Segment Anything Model
Yang, S., Tan, S., & Huang, J. (2024) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3658664.3659639 · Cited by 5
DiRLoc: Disentanglement Representation Learning for Robust Image Forgery Localization
Sheng, Z., Qu, Z., Lu, W., Cao, X., & Huang, J. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3522190 · Cited by 3
KLMN: Knowledge distillation based lightweight multi-clue image forgery detection and localization
Huang, H., Liu, Y., Jin, X., Xiao, S., & 刘, 斌. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888764 · Cited by 4
Context-Aware Weakly Supervised Image Manipulation Localization With SAM Refinement
Wang, X., Gong, T., Chu, Q., Liu, B., & Yu, N. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3592110 · Cited by 3
KARCI, H. D. (2023) · SOCIAL MENTALITY AND RESEARCHER THINKERS JOURNAL · doi:10.29228/smryj.68267 · Cited by 4
Metadata
- Authors
- Guan, H., Kozak, M., Robertson, E., Lee, Y., Yates, A. N., Delgado, A., Zhou, D., Kheyrkhah, T., Smith, J., & Fiscus, J.
- Affiliations
- National Institute of Standards and Technology (US)
- Thermo Fisher Scientific (Israel) (IL)
- Venue
- 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW)
- Publisher
- IEEE
- Published
- 2019-01
- Pages
- 63-72
- OpenAlex
- W2912308702
- Cited by
- 329
- References
- 31
- Citations by year
- 2026: 48 · 2025: 71 · 2024: 73 · 2023: 48 · 2022: 35 · 2021: 25 · 2020: 20 · 2019: 8
- FWCI
- 7.17
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Benford’s Law and Fraud Detection
- Keywords
- multimedia forensics, benchmark dataset, manipulated images, manipulated video, image forgery detection, video manipulation detection, data annotation, dataset collection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
