Development of Deepfake Detection Techniques for Protecting Multimedia Information using Deep Learning

The development of proposed deepfake detection techniques plays a prominent role in the safeguarding the multimedia information. The various challenges are addressed using the aid of deep learning techniques. This involves hybrid optimization techniques such as particle swarm optimization and genetic algorithms. This helps to improve the accuracy and efficiency in the detection of deepfake. PSO helps in optimizing the weights and parameters of the neural network to obtain faster convergence. GA helps in obtaining potential solutions to obtain the robust deepfake detection model. The integration of deep learning with hybrid optimization involves the collection and preprocessing of the multimedia information and proceeds to an optimization algorithm. The manipulated content in the online platform are detected during the training process. The hybrid optimization tcehniques helps in obtaining model generability and achieving resilience in various attacks occurring in the deepfake generation. The integration of PSO and GA helps in the accurate detection of deepfake content through continuous training and evaluation. Thus the proposed system forms a safeguarding tool for multimedia content and helps to eliminate various risks.