ABSTRACT
The seminar is about a meta-learning approach to deepfake detection models. Deepfake techniques
are becoming more advanced. Meta-learning allows a model to quickly adapt to new types of attacks
by leveraging previous experience. The model is trained on a large dataset of diverse deepfake
examples to learn the general patterns of forgery, and then fine-tuned on a smaller dataset of specific
deepfake attacks to deepfake detection. Data augmentation techniques can further enhance the
model's generalization ability. This seminar will discuss the theoretical aspects of meta-learning, data
augmentation techniques, and their applications in deepfake detection.
REFERENCES
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SIGNATURE OF PROJECT GUIDE:
SIGNATURE OF SEMINAR COORDINATOR: