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limitations of traditional detection models, which often [3] Balaji Y., Sankaranarayanan S., and Chellappa R.
struggle to generalize to previously unseen deepfake (2018). Metareg refers to the use of meta-
generation methods, this research explored Meta-Learning regularization to achieve spatial generalization.
for Domain Generalization (MLDG) as a potential solution. Neural data preparation frameworks.
By treating each deepfake generation technique as a distinct [4] Batagelj,B.,Peer,P.,Struc,V.,&Dobrisek,S.(2021).How
domain, this study assessed the model’s ability to learn and can I correctly recognize face masks for Covid-19 from
generalize to novel manipulation types. A key research visual data? Connected sciences.
question was whether MLDG could outperform the [5]
standard domain generalization baseline, Empirical Risk Bengio, S., Y., Cloutier, J., and Gecsei, J. (1992). Run the
show while optimizing synaptic learning. Optimality
Minimization (ERM). Unlike conventional meta-learning
in Manufactured and Organic Neural Systems, 6–8.
or few-shot learning approaches that require some level of
adaptation using new data, MLDG is better suited for real- [6] Minister, C.M., and Religious Administrator, H. (2024).
world scenarios where no prior data from new deepfake Profound learning involves understanding established
methods is available. This reflects the practical challenges concepts and ideas. Springer Worldwide Distributing.
of deepfake detection, where models must remain effective Reference: https://doi.org/10.1007/978-3-031-
against previously unseen manipulations. 45468-4.
Additionally, this study explored the benefits of Self- [7] Choi, Y.; Choi, M.-J.; Kim, M. S.; Ha, J.-W.; Kim, S.; and
Blended Images (SBIs) for data augmentation, a Choo, J. (2017).
technique designed to introduce greater variability into [8]
training datasets by generating additional source domains. Stargan developed generative antagonistic systems to
understand images across multiple domains.
The underlying hypothesis was that increasing training
Acknowledgment for the 2018 IEEE/CVF Conference
domain diversity would enable the model to learn more
on Computer Vision and Design: 8789-8797.
consistent features across domains, ultimately improving
D. A. Coccomini, N. Messina, C. Gennaro, and F. Falchi.
its ability to detect novel deepfake manipulations.
(2021).
In baseline experiments, where models were trained without [9]
augmented data, MLDG did not consistently outperform Using efficientnet and vision transformers for video
ERM, despite its theoretical advantages in generalization. A deepfake location. ArXiv: abs/2107.02612. Deepfakes
possible explanation is the significant domain shift between (2020). Deepfakes:
training and testing data in deepfake detection, which may https://github.com/deepfakes/faceswap. Deng J.,
limit MLDG's ability to generalize effectively when training Dong W., Socher R., Li L.-J., Li K., & Fei-Fei L. (2009).
data lacks sufficient diversity. [10] Imagenet is a big, multi-leveled image database.
Acknowledgment for the 2009 IEEE Conference on
References
[1] Alhogail, A.A., and Alsabih, A. (2021). Machine ComputerVisionandDesign,pages248–255. Dong S.,
learning and common dialect preparation are used to Wang J., Ji R., Liang J., Fan H., and Ge Z. (2022).
distinguish phishing emails. Computer Security, 110, VerifiableCharacterSpillage:
102414. [11] The shaky square is moving forward. Deepfake
[2] Altuncu, E., Franqueira, V.N.L., and Li, S. (2022). location generalization. 2023 IEEE/CVF Conference
on Computer Vision and Design Acknowledgement
Deepfake includes definitions, execution measures,
(CVPR), 3994–4004. Finn, C. (2022). Space
datasets, and a meta-review.
generalization [Part of the course CS]

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