FractalForensics is a proactive deepfake detection method that utilizes robust fractal watermarks to address the challenges posed by high-quality synthetic images, which often evade passive detectors. Unlike current detection techniques that primarily focus on identifying deepfakes without providing localization or clear explanations, FractalForensics aims to enhance detection performance by improving the stability of embedded watermarks. This approach not only facilitates more reliable identification of manipulated content but also offers the potential for precise localization of altered regions within images. The study highlights the limitations of existing methods, emphasizing their lack of interpretability and localization capabilities. By embedding fractal watermarks proactively, FractalForensics proposes a promising solution to these issues, contributing to more effective and transparent deepfake detection. This advancement aligns with ongoing research efforts to improve the robustness and explainability of synthetic media detection tools.