EfficientNet-Based Deepfake Detection: A Robust Approach for Real and Fake Media Classification

Deepfakes are an up-and-coming form of synthetic media-previously unrealistically realistic video, image, audio, or even live broadcast montaging faces or voices to create an impression of likeness. While opening new ways of creative expression, deepfakes create serious problems in many domains: misinformation, identity theft, defamation-the list increasingly goes on-jeopardizing the reliability of visual evidence in the modern world. As manipulations grow more and more sophisticated, the need to apply effective detection methods is critical. The present work is devoted to the application of the deep convolutional neural network EfficientNetB0 for deepfake detection. EfficientNetB0 is a scalable and computationally efficient architecture that will be applied in the differentiation of real and manipulated images by subtle irregularities in facial features and textures often invisible to the human naked eye. This model is trained on both real and deepfake images, with its optimized configuration to find these minute differences. Data augmentation techniques combined with regularization techniques, including dropout and global average pooling, enhance the capability of the model for generalization over diverse datasets. It achieved 90% on both training and validation, hence did a great job in detecting deepfakes. The confusion matrix further justifies the efficiency of the model in correctly classifying real and fake images. EfficientNetB0 has tremendous potential and can be very useful as a tool in mitigating risks due to manipulated media by helping one contribute toward preserving digital information integrity in an ever-evolving landscape.