The k-means clustering-based sampling approach for the fake class may face potential limitations or drawbacks that need to be addressed for optimal performance:
Cluster Overlap: One limitation is the possibility of overlapping clusters, especially when fake samples generated by different algorithms exhibit similarities. This can lead to confusion in sampling and may affect the model's ability to generalize effectively. To address this, the number of clusters can be adjusted iteratively to minimize overlap and ensure distinct clusters represent unique fake sample characteristics.
Cluster Size Variability: Another drawback could be the variability in cluster sizes, where some clusters may contain significantly more samples than others. This imbalance can impact the sampling process and bias the model towards larger clusters. Implementing a weighting mechanism based on cluster sizes or adjusting the sampling strategy to account for cluster imbalances can help mitigate this issue.
Optimal Cluster Determination: Determining the optimal number of clusters for k-means clustering can be challenging, especially in scenarios with diverse fake sample distributions. Fine-tuning the clustering process through iterative adjustments and evaluating cluster separability can help identify the most suitable clustering configuration for effective sampling.
By addressing these limitations through iterative refinement of the clustering process, adjusting cluster sizes, and optimizing the cluster determination, the k-means clustering-based sampling approach can be enhanced to improve the quality of sampled fake data points for training deepfake detection models.