The proposed sampling approach based on neural collapse and k-means clustering can be extended to other domains beyond audio deepfake detection, such as image or video deepfake detection, by adapting the methodology to suit the specific characteristics of these domains.
For image deepfake detection, the penultimate embeddings of deep learning models trained on image datasets can be utilized to identify confidently classified real and fake image samples. By applying k-means clustering on these embeddings, representative samples from diverse datasets can be selected based on their distance from cluster centers. This approach can help in creating a new training database that captures the variability within the fake class while ensuring generalization across unseen image data.
Similarly, in video deepfake detection, the temporal aspect of video data can be incorporated into the sampling approach. By considering features extracted from video frames or sequences, the methodology can be adjusted to sample representative video segments for training deepfake detection models. Clustering techniques can be applied to capture the unique characteristics of fake video samples generated using different algorithms, enhancing the model's ability to generalize across various video deepfake scenarios.
Overall, by adapting the neural collapse-based sampling approach to the specific data structures and characteristics of image and video domains, it can effectively enhance generalization in deepfake detection models beyond audio.