Beyond Exposing of Deepfake Exposed: A Fusion of Object Detection and DenseNet for Superior Recognition

The study involves deploying a DenseNet structure that combines object recognition and classification methods to develop a unique algorithm model for deep fake detection. With a 94% accuracy rate, this new technology has the potential to streamline and improve health services. Compared to the 3. 0%, the new approach outdid the old methods seven times. Some of the significant results are a decrease in false positive cases of 3% and a detection accuracy improvement of 5% over traditional CNN compared to conventional convolutional neural networks. Research utilizes an advanced data set thoroughly preprocessed process which is aimed at improving the model performance and obtained from external sources. The rising effectiveness in catching deepfakes thanks to DenseNet in object identification and image classification seems as the best solution: deepfake content will be taken down instantly.