DeepFake Detection for Military Security: A Deep Learning Approach Using TensorFlow and CNN Architecture

Military security faces considerable danger from DeepFake technology because adversaries can use its capabilities to modify visual media alongside the dissemination of fake information and the execution of cyber-psychological warfare. A deep learning artificial intelligence model for military DeepFake detection uses Convolutional Neural Networks (CNNs) combined with EfficientNetB4 architecture. Our model succeeds in differentiating between genuine images and deepfakes using TensorFlow on physical and simulated images obtained from the DeepFake Detection Challenge while achieving elevated accuracy numbers during training. The model benefits from transfer learning as well as data augmentation and advanced feature extraction mechanisms to detect a wide range of DeepFake generation approaches. The test datasets yield an accuracy of 89.36% which proves that CNN-based models have strong capabilities to detect altered media content. The integrated military intelligence systems will deploy this approach to perform instant authentication of digital images and strategic communications thus reducing the dangers from DeepFake-based deception operations. Researchers will enact two goals: they will work on strengthening adversarial attack resilience and create light-weight systems for resource-limited applications. DeepFake threats to national security require immediate protection which can be achieved through AI-armed countermeasures.