Unregulated accessibility to the latest deepfake technologies presents escalating, unprecedented threats to 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. This research presents a completely new way of detecting manipulation by looking for second-order spatiotemporal inconsistencies in chromatic energy distributions, as opposed to existing deepfake detection methods that rely on complicated multi-stream architectures or first-order pixel-level features. The theoretical importance comes from the fact that generative models can convincingly copy static visual features, but they always fail to keep colour and texture changes that make sense in both space and time. The Chromatic Gradient Anomaly Network (CrGAN) 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.  This method is useful in two ways: first, it gets state-of-the-art detection accuracy without needing complicated multi-modal fusion; second, and more importantly, it lets forensic analysts see exactly where and how a video was changed at the pixel level, which is very important for legal and investigative purposes. 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, revealing the synthesis boundary of temporally sparse flickers and the physically implausible discontinuities of the blend. The results for CrGAN demonstrate 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 detected region that is statistically differentiated from other detection models, achieving state-of-the-art performance while maintaining architectural simplicity. This is a big change in how deepfake detection works: it moves complexity from model architecture to forensic signal representation, which makes the solution more elegant, easier to understand, and more generalizable.
In conclusion, this study validates how targeting 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.