Enhanced Deepfake Identification with Efficientnetb4 and Transfer Learning for Improved Media Verification

The fast progression of DeepFake technology brings about severe dangers for digital media stability, notoriously enabling fake videos that are very comparable. This altered content can be highly hazardous to security, privacy, and public trust, specifically in cases of misinformation, political manipulation, and identity theft. Most of the existing detection methods fail to achieve accuracy, efficiency of computation, and ability to adapt to the variety of Deep Fake types. This paper tries to boost up DeepFake detection by taking the compound scaling and high efficiency of the state-of-the-art visual classification model, the EfficientNetB4-constructed convolutional neural network. The work employs the DeepFake Faces part of the DeepFake Detection Challenge (DFDC) dataset, in which labeled frames are taken out of genuine and manipulated videos. Preprocessing steps include frame extraction and dataset balancing for the learning to be efficient. The video set is then processed using the EfficientNetB4 model, which has been trained to categorize these excerpts based on features derived from the amount of high-level abstraction obtained by learning what makes the truly genuine and forged facial representations looked at. Experimental results indicate that the method proposed above can achieve 89.36 % detection accuracy and reduce the training loss to 0.14 successfully, indicating strong convergence and performance strong axiom. These outcomes show the skills of EfficientNetB4 in detecting complicated DeepFake Data patterns and add to the development of effective tools for media confirmation and digital forensics.