Catching Deepfakes in Real-Time: A Spatial-Temporal Approach with EfficientNet-B0 and Bi-LSTM

Catching Deepfakes in Real-Time: A Spatial-Temporal Approach with EfficientNet-B0 and Bi-LSTM The problem with most early deepfake detection models is that they treat video as a collection of static images. They pass individual frames through a Convolutional Neural Network (CNN) and look for spatial artifacts—weird blurring around the jawline, mismatched skin tones, or pixelated boundaries. But modern deepfakes (especially those generated by GANs and diffusion models) have virtually eliminated static spatial artifacts. A single frame often looks flawless. What gives a deepfake away isn't the space ; it is the time . The blink rate is unnatural. The micro-expressions jitter. The lip-sync drifts off by a fraction of a second. To catch a modern deepfake, you cannot just look at a picture. You have to understand the sequence. Here is how I built a Spatial-Temporal Deepfake Detector using PyTorch, combining an EfficientNet-B0 backbone for spatial feature extraction with a Bi-LSTM network fo