The proliferation of highly realistic deepfakes, powered by Generative Artificial Intelligence (GenAI), presents significant challenges to digital trust and security. This survey provides a comprehensive overview of proactive deepfake detection approaches, including disruption and watermarking methods. Our survey provides a taxonomy of these strategies based on their existing methodologies and extend the discussion to other perspectives, including imperceptibility, transferability, universality, and robustness. We also explore the associated threat models, considering various adversary objectives and capabilities. Additionally, we review state-of-the-art deepfake generation techniques that provide context for the challenges faced by detection methods.