While the current set of detection methods integrated into the platform are state-of-the-art, they may still have limitations and biases that could impact the platform's overall reliability. One potential limitation is the generalization ability of the detectors across different generative models. Some detectors may perform well on specific types of deepfakes but struggle with others, leading to false positives or false negatives.
Biases in the training data used to develop the detection models can also introduce limitations. If the training data is not diverse enough or if it contains inherent biases, the detectors may not perform effectively on real-world scenarios. Addressing these limitations requires a multi-faceted approach. Firstly, conducting regular audits and evaluations of the detection algorithms to identify and mitigate biases is essential. This can involve retraining the models on more diverse datasets to improve their generalization capabilities.
Moreover, implementing ensemble methods that combine multiple detectors can help mitigate individual biases and improve overall detection accuracy. By aggregating the outputs of different detectors, the platform can make more informed decisions and reduce the impact of biases present in individual models. Additionally, incorporating explainable AI techniques can provide insights into the decision-making process of the detectors, helping to identify and address biases effectively.