Open Access
| Issue |
EPJ Web Conf.
Volume 344, 2025
AI-Integrated Physics, Technology, and Engineering Conference (AIPTEC 2025)
|
|
|---|---|---|
| Article Number | 01019 | |
| Number of page(s) | 9 | |
| Section | AI-Integrated Physics, Technology, and Engineering | |
| DOI | https://doi.org/10.1051/epjconf/202534401019 | |
| Published online | 22 December 2025 | |
- S. Singh and A. Dhumane, Unmasking digital deceptions: An integrative review of deepfake detection, multimedia forensics, and cybersecurity challenges. MethodsX. 15, 103632 (2025). https://doi.org/10.1016/j.mex.2025.103632 [Google Scholar]
- W. Matli, Extending the theory of information poverty to deepfake technology. Int. J. Inf. Manag. Data Insights. 4, 2, 100286 (2024). https://doi.org/10.1016/j.jjimei.2024.100286 [Google Scholar]
- M. Islam, H. Batool, N. Ahtasham, and Z. Muhammad, AI Threats to Politics, Elections, and Democracy: A Blockchain-Based Deepfake Authenticity Verification Framework. Blockchains. 2, 458–481 (2024). https://doi.org/10.3390/blockchains2040020 [Google Scholar]
- P. Edwards, J.-C. Nebel, D. Greenhill, and X. Liang, A Review of Deepfake Techniques: Architecture, Detection, and Datasets. IEEE Access. 12, 154718–154742 (2024). https://doi.org/10.1109/ACCESS.2024.3477257 [Google Scholar]
- V. Sunkari and A. Nagesh, Artificial intelligence for deepfake detection: systematic review and impact analysis. IAES Int. J. Artif. Intell. 13, 3786 (2024). https://doi.org/10.11591/ijai.v13.i4.pp3786-3792 [Google Scholar]
- A. Diel, T. Lalgi, I. C. Schröter, K. F. MacDorman, M. Teufel, and A. Bäuerle, Human performance in detecting deepfakes: A systematic review and meta- analysis of 56 papers. Comput. Hum. Behav. Reports. 16, 100538 (2024). https://doi.org/10.1016/j.chbr.2024.100538 [Google Scholar]
- J. Jheelan and S. Pudaruth, Using Deep Learning to Identify Deepfakes Created Using Generative Adversarial Networks. Computers. 14, 60 (2025). https://doi.org/10.3390/computers14020060 [Google Scholar]
- C.-J. Chew, Y.-C. Lin, Y.-C. Chen, Y.-Y. Fan, and J.-S. Lee, Preserving manipulated and synthetic Deepfake detection through face texture naturalness. J. Inf. Secur. Appl. 83, 103798 (2024). https://doi.org/10.1016/j.jisa.2024.103798 [Google Scholar]
- R. Sunil, P. Mer, A. Diwan, R. Mahadeva, and A. Sharma, Exploring autonomous methods for deepfake detection: A detailed survey on techniques and evaluation. Heliyon. 11, 3, e42273 (2025). https://doi.org/10.1016/j.heliyon.2025.e42273 [Google Scholar]
- Y. Nan, J. Ju, Q. Hua, H. Zhang, and B. Wang, A- MobileNetV2: An approach of facial expression recognition. Alexandria Eng. J. 61, 6, 4435–4444 (2022). https://doi.org/10.1016/j.aej.2021.09.066 [Google Scholar]
- A. Kaur, A. Noori Hoshyar, V. Saikrishna, S. Firmin, and F. Xia, Deepfake video detection: challenges and opportunities. Artif. Intell. Rev. 57, 6, 159 (2024). https://doi.org/10.1007/s10462-024- 10810-6 [Google Scholar]
- J. Chai, H. Zeng, A. Li, and E. W. T. Ngai, Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Mach. Learn. with Appl. 6, 100134 (2021). https://doi.org/10.1016/j.mlwa.2021.100134 [Google Scholar]
- A. Agarwal and N. Ratha, Detection of identity swapping attacks in low-resolution image settings. J. Inf. Secur. Appl. 89, 103911 (2025). https://doi.org/10.1016/j.jisa.2024.103911 [Google Scholar]
- A. Heidari, N. J. Navimipour, H. Dag, S. Talebi, and M. Unal, A Novel Blockchain-Based Deepfake Detection Method Using Federated and Deep Learning Models. Cognit. Comput. 16, 3, 1073–1091 (2024). https://doi.org/10.1007/s12559-024- 10255-7 [Google Scholar]
- S. Sadiq, T. Aljrees, and S. Ullah, Deepfake Detection on Social Media: Leveraging Deep Learning and FastText Embeddings for Identifying Machine-Generated Tweets. IEEE Access. 11, 95008–95021 (2023). https://doi.org/10.1109/ACCESS.2023.3308515 [Google Scholar]
- A. H. Khalifa, N. A. Zaher, A. S. Abdallah, and M. W. Fakhr, Convolutional Neural Network Based on Diverse Gabor Filters for Deepfake Recognition. IEEE Access. 10, 22678–22686 (2022). https://doi.org/10.1109/ACCESS.2022.3152029 [Google Scholar]
- A. Wibowo, S. R. Purnama, P. W. Wirawan, and H. Rasyidi, Lightweight encoder-decoder model for automatic skin lesion segmentation. Informatics Med. Unlocked. 25, 100640 (2021). https://doi.org/10.1016/j.imu.2021.100640 [Google Scholar]
- F. Chen and J. Y. Tsou, Assessing the effects of convolutional neural network architectural factors on model performance for remote sensing image classification: An in-depth investigation. Int. J. Appl. Earth Obs. Geoinf. 112, 102865 (2022). https://doi.org/10.1016/j.jag.2022.102865 [Google Scholar]
- I. Rodriguez-Lujan, C. Santa Cruz, and R. Huerta, Hierarchical linear support vector machine. Pattern Recognit. 45, 12, 4414–4427 (2012). https://doi.org/10.1016/j.patcog.2012.06.002 [Google Scholar]
- G. Wen-wen, Y. Lv, Y. Jia-yu, Z. Wang, and S. Yuan-hai, Fast support vector classifier with generalization-memorization kernel. Procedia Comput. Sci. 214, 55–62 (2022). https://doi.org/10.1016/j.procs.2022.11.148 [Google Scholar]
- Y.-J. Chang, Y.-L. Lin, and P.-F. Pai, Support Vector Machines with Hyperparameter Optimization Frameworks for Classifying Mobile Phone Prices in Multi-Class. Electronics. 14, 2173 (2025). https://doi.org/10.3390/electronics14112173 [Google Scholar]
- X. Chen, Z. Yin, and H. Tian, Support Vector Machines Principles and Actually Example. Procedia Comput. Sci. 243, 2–11 (2024). https://doi.org/10.1016/j.procs.2024.09.002 [Google Scholar]
- Y. Sun, Financial distress prediction based on deep learning model. Procedia Comput. Sci. 243, 1069–1078 (2024). https://doi.org/10.1016/j.procs.2024.09.127 [Google Scholar]
- D. Keerthana, V. Venugopal, M. K. Nath, and M. Mishra, Hybrid convolutional neural networks with SVM classifier for classification of skin cancer. Biomed. Eng. Adv. 5, 100069 (2023). https://doi.org/10.1016/j.bea.2022.100069 [Google Scholar]
- R. Ogundokun, S. Misra, A. Akinrotimi, and H. Ogul, MobileNetV2-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors. Sensors. 23, 656 (2023). https://doi.org/10.3390/s23020656 [Google Scholar]
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