Researchers at Keele University have built a deepfake image detector, FDIP-NET, that treats the brightness of a photograph like a physical field, borrowing mathematical ideas from fluid dynamics to expose synthetic faces. According to a study published in Frontiers in Imaging, the system reached mean held-out test accuracies of 98.654 percent on a face-manipulation dataset, 98.952 percent on a large composite deepfake collection, and 99.669 percent on a set of GAN-generated faces. On the widely used FaceForensics++ benchmark under c23 compression, the model achieved 91.00 percent frame-level accuracy on the official independent test partition. FDIP-NET converts each image’s luminance into a scalar field and derives three gradient-based descriptors from it: a divergence-like second-order response, a gradient magnitude, and a kinetic-energy-like quadratic response. The authors state this is a formal analogy rather than physics: no fluid motion is modeled, the Navier-Stokes equations are never solved, and the descriptors do not represent measured fluid quantities. The three normalized FDIP channels are concatenated with normalized RGB channels in the order D, S, K, R, G, B, producing a 6 by 320 by 320 tensor for a ResNet-like backbone whose first convolution accepts six channels. Training uses the AdamW optimizer with weight decay of 10 to the minus 4, Focal Loss with parameters alpha 0.5 and gamma 2.0