Dataset Difficulties and Prejudice in Deepfake Identification
The strength of deepfake detection depends on the quality of the training data. Deepfake detection methods run the risk of being ineffective or, worse, biased against particular populations in the absence of diverse, representative, and high-quality datasets. This chapter explores the ethical issues surrounding the use of open versus proprietary datasets, the difficulties in curating datasets for deepfake detection, and the function of data augmentation in enhancing model resilience. Managing Superior Deepfake Datasets: Diversity and Representativeness Concerns Any deepfake detection system must be trained on a high-quality, diverse, and well-labeled dataset in order to function properly. Curating such a dataset, however, comes with a number of difficulties, such as: Issues with Data Scarcity and Quality 1. Lack of Realistic Deepfake Samples: The majority of datasets are less reflective of the most recent AI-generated manipulations because they contain deepfakes produced wi...