Unregulated accessibility to the latest deepfake technologies presents escalating, unprecedented threats to the personal security, public trust, and democratic integrity, owing to the ever-increasing sophistication and realism of these forgeries. The biggest challenge is the inability of human verification to ascertain the original from the forgeries. Therefore, this research aims to establish an initial framework of detection and verification. The Chromatic Gradient Anomaly Network (ChrGAN) is an architecture that will be built and tested to capture changes of the various components of a video over time in order to reveal patterns of inconsistency between the spatiotemporal levels of a video and the changes of its chromatic components. One of the most important contributions of this research is the analysis of the second order derivatives (in this case, the Chromatic Gradient Fields) of the Spatiotemporal Chromatic Energy Distributions, leaving the synthesis boundary of the temporally sparse flickers and the physically implausible discontinuities of the blend exemplified by the gap. The results for the CrGAN show the highest level of diagnostic confidence, reporting a detection rate of 97.9%, and most importantly a level of pixel-wise localized mapping of the region detected that is statistically differentiated from the other detection models for a state of the art performance measurement in a machine learning model for the detection only. In conclusion, this study validates how targeting the second-order spatiotemporal inconsistencies using chromatic gradients, not only acts as an efficient detection mechanism, but also as an interpretable tool in the combat against digital deception by identifying the how and where of video forgery.