Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection
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Deepfake Generation Techniques are evolving at a rapid pace, making it possible to create realistic manipulated images and videos and endangering the serenity of modern society. The continual emergence of new and varied techniques brings with it a further problem to be faced, namely the ability of deepfake detection models to update themselves promptly in order to be able to identify manipulations carried out using even the most recent methods. This is an extremely complex problem to solve, as training a model requires large amounts of data, which are difficult to obtain if the deepfake generation method is too recent. Moreover, continuously retraining a network would be unfeasible. In this paper, we ask ourselves if, among the various deep learning techniques, there is one that is able to generalise the concept of deepfake to such an extent that it does not remain tied to one or more specific deepfake generation methods used in the training set. We compared a Vision Transformer with an EfficientNetV2 on a cross-forgery context based on the ForgeryNet dataset. From our experiments, It emerges that EfficientNetV2 has a greater tendency to specialize often obtaining better results on training methods while Vision Transformers exhibit a superior generalization ability that makes them more competent even on images generated with new methodologies.
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The attention mechanism enables the model to abstract better the concept of deepfake, but only with the constraint of availability of a large quantity and diversity of data used during training.
“This result probably stems from the fact that the training set consists of significantly more images than in the previous setup and it is strongly in line with what is presented in [39].”
From On the Generalization of Deep Learning Models in Video Deepfake Detection (2023) · cites this paper as [39]
The Conclusions report that Vision Transformers better generalise the concept of deepfake than convolutional networks. Section 4.2 explores whether training on varied generation methods improves generalisation, but does not establish that an attention mechanism causes the advantage or that a large, diverse training set is required. Section 4.1 also reports generalisation when training used only one generation method at a time. Page numbers were not available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe Vision Transformer's superior generalization stems from the larger quantity and diversity of training data, which enables it to abstract the concept of deepfake better.
“This result probably stems from the fact that the training set consists of significantly more images than in the previous setup and it is strongly in line with what presented in [39].”
From On the Generalization of Deep Learning Models in Video Deepfake Detection (2023) · cites this paper as [39]
Sections 4.2 and 4.3 report that the Vision Transformer generalizes better across forgery methods and suggest that training on varied methods might improve generalization. They do not establish that its advantage stems from more or more diverse training data: both architectures were trained on the same sub-datasets, and Section 4.3 reports the Transformer's lower variance regardless of the methods used to build the training set. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe Vision Transformer's superior generalization in the multiple methods training setup probably stems from the training set consisting of significantly more images than in the previous setup.
“This result probably stems from the fact that the training set consists of significantly more images than in the previous setup and it is strongly in line with what is presented in [39].”
From On the Generalization of Deep Learning Models in Video Deepfake Detection (2023) · cites this paper as [39]
EfficientNetV2 performs better in training techniques, but ViTs are more proficient in generalisation, making them superior in detecting deepfakes.
“EfficientNetV2 performs better in training techniques, but ViTs are more proficient in generalisation, making them superior in detecting deepfakes.”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Coccomini et al. 2022
Coccomini et al. (2022) compared CNNs and Vision Transformers (ViTs) for deepfake image detection using the ForgeryNet dataset to assess cross-forgery performance.
“Coccomini etal. (2022) compare CNNs and Vision Transformers (ViTs) in the context of deepfake image detection. They used the ForgeryNet dataset to assess the efficiency of their cross-forgery performance.”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Coccomini et al. 2022
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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 · see citing works
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
Analyzing and Improving the Image Quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00813 · Cited by 6,332 · see citing works
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2603342 · Cited by 6,214 · see citing works
Generative Adversarial Networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.2661 · Cited by 4,477 · see citing works
StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation
Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., & Choo, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00916 · Cited by 4,163 · see citing works
FaceForensics++: Learning to Detect Manipulated Facial Images
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niessner, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00009 · Cited by 3,314 · see citing works
Albumentations: fast and flexible image augmentations
Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., & Kalinin, A. A. (2020) · Information · doi:10.3390/info11020125 · Cited by 2,714 · see citing works
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification
Chen, C.-F. R., Fan, Q., & Panda, R. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00041 · Cited by 2,242 · see citing works
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics
Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00327 · Cited by 1,846 · cited 2× · see citing works
“[29], Celeb-DF [26], Google Deepfake Detection Dataset [10] and the more recent DFDC [9], Deepforensics [16] and ForgeryNet [14].”
MethodLearning to Discover Cross-Domain Relations with Generative Adversarial Networks
Kim, T., Cha, M., Kim, H., Lee, J. K., & Kim, J. (2017) · arXiv · doi:10.48550/arxiv.1703.05192 · Cited by 1,716 · see citing works
EfficientNetV2: Smaller Models and Faster TrainingPreprint
Tan, M., & Le, Q. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00298 · Cited by 1,473 · see citing works
Exposing Deep Fakes Using Inconsistent Head Poses
Yang, X., Li, Y., & Lyu, S. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683164 · Cited by 1,196 · see citing works
MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
Lee, C.-H., Liu, Z., Wu, L., & Luo, P. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00559 · Cited by 1,130 · see citing works
FSGAN: Subject Agnostic Face Swapping and Reenactment
Nirkin, Y., Keller, Y., & Hassner, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00728 · Cited by 706 · see citing works
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
Jiang, L., Li, R., Wu, W., Qian, C., & Loy, C. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00296 · Cited by 599 · see citing works
First Order Motion Model for Image Animation
Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., & Sebe, N. (2020) · arXiv · doi:10.48550/arxiv.2003.00196 · Cited by 585 · see citing works
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
Korshunov, P., & Marcel, S. (2018) · arXiv · doi:10.48550/arxiv.1812.08685 · Cited by 497 · see citing works
Hierarchical Cross-Modal Talking Face Generation With Dynamic Pixel-Wise Loss
Chen, L., Maddox, R. K., Duan, Z., & Xu, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00802 · Cited by 437 · see citing works
The deepfake detection challenge (dfdc) dataset
Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M., & Ferrer, C. C. (2020) · arXiv · doi:10.48550/arxiv.2006.07397 · Cited by 427 · see citing works
SC-FEGAN: Face Editing Generative Adversarial Network With User’s Sketch and Color
Jo, Y., & Park, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00183 · Cited by 346 · see citing works
Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning
Deng, Y., Yang, J., Chen, D., Wen, F., & Tong, X. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00520 · Cited by 329 · see citing works
Combining EfficientNet and Vision Transformers for Video Deepfake Detection
Coccomini, D. A., Messina, N., Gennaro, C., & Falchi, F. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-06433-3_19 · Cited by 296 · see citing works
Self-citationAdvancing High Fidelity Identity Swapping for Forgery Detection
Li, L., Bao, J., Yang, H., Chen, D., & Wen, F. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00512 · Cited by 288 · see citing works
Text-based Editing of Talking-head Video
Fried, O., Tewari, A., Zollhöfer, M., Finkelstein, A., Shechtman, E., Goldman, D. B., Genova, K., Jin, Z., Theobalt, C., & Agrawala, M. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3323028 · Cited by 270 · see citing works
TweepFake: about detecting deepfake tweets
Fagni, T., Falchi, F., Gambini, M., Martella, A., & Tesconi, M. (2021) · PLOS ONE · doi:10.1371/journal.pone.0251415 · Cited by 185 · see citing works
Self-citationForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis
He, Y., Gan, B., Chen, S., Zhou, Y., Yin, G., Song, L., Sheng, L., Shao, J., & Liu, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00434 · Cited by 161 · see citing works
DeepFake video detection using convolutional vision transformer
Wodajo, D., & Atnafu, S. (2021) · arXiv · doi:10.48550/arxiv.2102.11126 · Cited by 147 · see citing works
Optical Flow based CNN for detection of unlearnt deepfake manipulations
Caldelli, R., Galteri, L., Amerini, I., & Del Bimbo, A. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.03.005 · Cited by 115 · see citing works
Self-citationDeepFaceLab: A simple, flexible and extensible face swapping framework.
Perov, I., Gao, D., Chervoniy, N., Liu, K., Marangonda, S., Umé, C., Dpfks, Facenheim, C. S., Rp, L., Jiang, J., et al. (2020) · Cited by 106 · see citing works
DeepFaceLab: Integrated, flexible and extensible face-swapping frameworkPreprint
Perov, I., Gao, D., Chervoniy, N., Liu, K., Marangonda, S., Umé, C., Dpfks, Facenheim, C. S., Rp, L., Jiang, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.05535 · Cited by 102 · see citing works
Deepfake Detection Scheme Based on Vision Transformer and DistillationPreprint
Heo, Y. J., Choi, Y. J., Lee, Y., & Kim, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.01353 · Cited by 76 · see citing works
Jointly Embedding Multiple Single-Cell Omics Measurements
Liu, J., Huang, Y., Singh, R., Vert, J., & Noble, W. S. (2019) · PubMed · doi:10.4230/lipics.wabi.2019.10 · Cited by 67 · see citing works
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image ClassificationPreprint
Chen, C.-F., Fan, Q., & Panda, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.14899 · Cited by 23 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [18]18. Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8110-8119.
- [33]33. Mingxing Tan and Quoc V. Le. 2021. EfficientNetV2: Smaller Models and Faster Training. (2021). https://doi.org/10.48550/ARXIV.2104.00298 · DOI
Cited by
Sorted by citations
Maurício, J., Domingues, I., & Bernardino, J. (2023) · Applied Sciences · doi:10.3390/app13095521 · Cited by 617 · see citing works
Deepfake video detection: challenges and opportunities
Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10810-6 · Cited by 214 · see citing works
“Coccomini et al. (2022) compare CNNs and Vision Transformers (ViTs) in the context of deepfake image detection.”
Towards Multimodal Disinformation Detection by Vision-language Knowledge Interaction
Li, Q., Gao, M., Zhang, G., Zhai, W., Chen, J., & Jeon, G. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102037 · Cited by 46 · see citing works
Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection
Xu, Y., Liang, J., Sheng, L., & Zhang, X. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02054-2 · Cited by 41 · see citing works
Fsbi: Deepfake detection with frequency enhanced self-blended images
Abul Hasanaath, A., Luqman, H., Katib, R., & Anwar, S. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105418 · Cited by 34 · see citing works
MINTIME: Multi-Identity Size-Invariant Video Deepfake Detection
Coccomini, D. A., Zilos, G. K., Amato, G., Caldelli, R., Falchi, F., Papadopoulos, S., & Gennaro, C. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3409054 · Cited by 33 · see citing works
Self-citationA Review of Deep Learning-based Approaches for Deepfake Content Detection
Passos, L. A., Jodas, D., Costa, K. A. P., Júnior, L. A. S., Rodrigues, D., Ser, J. D., Camacho*, D., & Papa, J. (2023) · doi:10.22541/au.169735672.27713914/v1 · Cited by 116 · see citing works
On the Generalization of Deep Learning Models in Video Deepfake Detection
Coccomini, D. A., Caldelli, R., Falchi, F., & Gennaro, C. (2023) · Journal of Imaging · doi:10.3390/jimaging9050089 · Cited by 32 · see citing works
“…extracted frames are pre-processed, similar to many other deepfake detection methods [8–10, 22,25] by introducing a face extraction The present paper is derived from another work [39] where we already conducted a similar cross-forgery analysis on the part of the dataset consisting of still images.”
MethodResultSelf-citationVideo authentication detection using deep learning: a systematic literature review
Alrawahneh, A. A.-M., Abdullah, S. N. H. S., Abdullah, S. N. H. S., Kamarudin, N. H., & Taylor, S. K. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05997-8 · Cited by 24 · see citing works
Detecting images generated by diffusers
Coccomini, D. A., Esuli, A., Falchi, F., Gennaro, C., & Amato, G. M. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2127 · Cited by 23 · see citing works
Self-citationDeepFake video detection: Insights into model generalisation — A Systematic review
Ramanaharan, R., Guruge, D. B., & Agbinya, J. I. (2025) · Data and Information Management · doi:10.1016/j.dim.2025.100099 · Cited by 25 · see citing works
Zhang, G., Gao, M., Li, Q., Zhai, W., & Jeon, G. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10316-x · Cited by 20 · see citing works
A comprehensive study on multimedia DeepFakes
Boutadjine, A., Harrag, F., Shaalan, K. F., & Karboua, S. (2023) · International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) · doi:10.1109/icaeccs56710.2023.10104814 · Cited by 13 · see citing works
Using Deep Learning to Identify Deepfakes Created Using Generative Adversarial Networks
Jheelan, J., & Pudaruth, S. (2025) · Computers · doi:10.3390/computers14020060 · Cited by 14 · see citing works
Li, Y., Zhang, Y., Yang, H., Chen, B., & Huang, D. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02091-x · Cited by 10 · see citing works
A Comparative Analysis of Compression and Transfer Learning Techniques in DeepFake Detection Models
Karathanasis, A., Violos, J., & Kompatsiaris, I. Y. (2025) · Mathematics · doi:10.3390/math13050887 · Cited by 10 · see citing works
Raza, A., Basit, A., Amin, A., Arfeen, Z. A., Masud, M. I., Fayyaz, U., & Jumani, T. A. (2026) · AI · doi:10.3390/ai7020068 · Cited by 18 · see citing works
Shahin, M., & Deriche, M. A. (2024) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd61670.2024.10548578 · Cited by 8 · see citing works
Are vision transformers replacing convolutional neural networks in scene interpretation?: A review
Rosy, N. A., Balasubadra, K., & Deepa, K. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07574-1 · Cited by 7 · see citing works
Toward Exploring Fairness in Visual Transformer Based Natural and GAN Image Detection Systems
Gangan, M. P., Kadan, A., & Lajish, V. L. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3509340 · Cited by 3 · see citing works
Vishal, V. M., Senthil Kumar, G., Sundarambal, B., Kirubakaran, N., Selvaganesan, C., & Shruthi, V. (2025) · International Conference on Data Science and Business Systems (ICDSBS) · doi:10.1109/icdsbs63635.2025.11031813 · Cited by 2 · see citing works
Martin, N., Nambayil, S. R., Devikrishna, U., Joel Jismon, P., & Fasila, K. A. (2024) · IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES) · doi:10.1109/spices62143.2024.10779815 · Cited by 2 · see citing works
James, U. U., Olarinoye, H. S., Uchenna, I. R., Idika, C. N., Ngene, O. J., Ijiga, O. M., & Itemuagbor, K. (2025) · Advances in Artificial Intelligence and Robotics Research · doi:10.4236/airr.2025.11004 · Cited by 2 · see citing works
FSBI: Deepfakes Detection with Frequency Enhanced Self-Blended ImagesPreprint
Hasanaath, A. A., Luqman, H., KATIB, R. F., & Anwar, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4869258 · Cited by 2 · see citing works
MAD '22 Workshop: Multimedia AI against Disinformation
Ionescu, B. E., Kordopatis-Zilos, G., Popescu, A., Cuccovillo, L., & Papadopoulos, S. (2022) · International Conference on Multimedia Retrieval · doi:10.1145/3512527.3531440 · Cited by 1 · see citing works
Leveraging Attention to Achieve Generalization for Image Forgery Detection
Atta, M. (2023) · Intelligent Methods, Systems, and Applications (IMSA) · doi:10.1109/imsa58542.2023.10217511 · Cited by 1 · see citing works
Future-Proofing Deepfake Detection by Integrating Audio, Video, and Text
Lisena, P., Lee, K. A., Wang, Y., Todisco, M., Galdi, C., Raphaël Troncy, A., Evans, N., Lin, W., Chau, L., & Mak, M. (2026) · ACM AI Letters · doi:10.1145/3797958 · Cited by 1 · see citing works
SURVEY: ANALYSIS AND DETECTION OF DEEPFAKE IN MULTIMEDIA THROUGH ADVANCED DEEP LEARNING APPROACHES
(2024) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets48361
Sadowska, E. (2023) · Studia Wschodnioeuropejskie · doi:10.31971/24500267.20.15
Fsbi: Deepfakes Detection with Frequency Enhanced Self-Blended ImagesPreprint
Hasanaath, A. A., Luqman, H., KATIB, R. F., & Anwar, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4876969
Enhancing Image Copy Detection through Dynamic Augmentation and Efficient Sampling with Minimal Data
Fawzy, M., Tawfik, N. S., & Saleh, S. N. (2024) · Electronics · doi:10.3390/electronics13163125
Detection of Facial Deep Fake Images using CNN Architecture
Telaprolu, G., Tallapally, S., Thakkalapelly, S. R., Vattam, M., & Shereesha, M. (2024) · International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET) · doi:10.1109/i3ceet61722.2024.10993881 · Cited by 1 · see citing works
A Comparative Review of Deep Learning Models for Deepfake Detection
Cogranne, R. (2025) · International Conference on Advanced Machine Learning and Data Science (AMLDS) · doi:10.1109/amlds63918.2025.11159448
ImageShield: Generalized DL Techniques for AI-Generated Deepfakes Image Detection
Prakash, O. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-4319-6_32
ELA-BLIP 2.0: Bridging Visual Forensics and Sentence Generation with ViT-Based Forgery Detection
Singh, C. P., Dabas, B., Shakya, D. K., & Verma, B. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10783-1_12
Countering Deepfakes: Optimizing Convolutional Networks and Vision Transformer with ECA and SE Block
Agustianto, S. H., Yunus, A. P., & Saputra, W. A. (2025) · doi:10.1109/icitee66631.2025.11338439
Towards Detection of Deep Fake Videos
Mohammed Muddasir, N., Abhishek, M., J, C. C. S., & K, D. M. (2025) · Asia Pacific Conference on Innovation in Technology (APCIT) · doi:10.1109/apcit65661.2025.11411376
Reza, M. S., Elias, F., Mahmud, M. I., & Ahmed, N. (2026) · PLoS ONE · doi:10.1371/journal.pone.0340099
A Systematic Review of Image Deepfake Detection: Datasets, Features, and Recent AdvancementsPreprint
Abbasalikashi, M. H., Nejati, F., Yau, K. A., Hao, H. M., Koohi, S. Z., & Bonab, M. B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6668912
Contractive Autoencoder Based Deepfake Image Detection
Srishti, K., Saroj, S. K., & Tiwari, R. K. (2026) · SN Computer Science · doi:10.1007/s42979-026-04993-8
Tom, T., Hariprasad, Y., Mohanty, P., & Puthussery, A. (2026) · Signals and communication technology · doi:10.1007/978-3-031-98036-7_5
Hariharasudhan, S., Risheeth, P. S. L., & Satyanarayana, G. V. V. (2026) · International Conference on Artificial Intelligence & Machine Learning in Communication and Power Systems (AIMLCPS) · doi:10.1109/aimlcps68702.2026.11542454
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Kaur, D., & Kumar, M. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-28379-5_10
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Waoo, A., Verma, A., Singh, A., Meena, K. B., & Tyagi, V. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9285-1.ch006
Deepfake Image Detection Using Spatial Rich Model Features and Deep Learning Fusion
Adnane, E., & Bogacsovics, G. (2026) · IEEE Conference on Information Technology and Data Science (CITDS) · doi:10.1109/citds70571.2026.11706841
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Ahmed, N. U. R., Badshah, A., Tajammul, A., Alghushairy, O., Alharbey, R., & Daud, A. (2026) · Journal of Advances in Information Technology · doi:10.12720/jait.17.10.1842-1866
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Self-citation
- Authors
- Coccomini, D. A., Caldelli, R., Falchi, F., Gennaro, C., & Amato, G.
- Affiliations
- Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT)
- Consorzio Nazionale Interuniversitario per le Telecomunicazioni (IT)
- Venue
- Proceedings of the 1st International Workshop on Multimedia AI against Disinformation
- Publisher
- ACM
- Published
- 2022-06-27
- Pages
- 52-58
- OpenAlex
- W4283452332
- Cited by
- 47
- References
- 33
- Citations by year
- 2026: 13 · 2025: 11 · 2024: 16 · 2023: 6 · 2022: 1
- FWCI
- 3.08
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- arXiv (Cornell University) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- ISTI Open Portal (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- AI4Media (951911)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, vision transformer, EfficientNetV2, convolutional neural networks, image forgery detection
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 ›
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