The developments in home computers, united with the thousands upon thousands of images/videos of individuals present on the Internet, allowed for the proliferation of deepfaked media affecting the lives of private individuals and the dangerous spread of misinformation. Current state-of-the art detection methods show impressive results. However the development of improved generation methods overcomes them, as there are generalization difficulties. This paper explores the viability of use of implicit representations of facial videos on deepfake detection. Implicit representations offer computer vision tasks a new paradigm of research, possibly offering alternatives to the current methods based on the color space or frequency domain. This work investigates the use of Sinusoidal Representation Networks (SIRENs) to show a significant difference between Fréchet Video Distance (FVD) scores obtained from bonafide videos and their SIREN reconstruction and deepfake videos and their SIREN reconstruction. This result leads to the conclusion that the SIREN representation of a video can be used as input for a deepfake detection method, opening a new avenue of research.