Enhancing Deepfake Detection Through Hybrid MobileNet-LSTM Model with Real-Time Image and Video Analysis

Abstract: The race to crush information integrity and public trust is being won by one thing: deepfakes, AI manipulated media. The goal is to enhance our Deepfake detection using MobileNet and LSTM network. MobileNet’s lightweight CNN architecture is able to coreinduce thevisual features in an image by an image and in avideoand can pick up the slight visual clues of textures and facial structure. A temporal inconsistencies that cannotbeseenby image base methods are then analyzed using an LSTM network. The hybrid model is trained on real and deepfake media datasets, and is thus adaptable to emerging deepfake techniques. This has auserfaceinterfacetoanalyzethereal time and fly mediatogettheanalysisandanalysisscoreand visual feedback of the identified artifacts. Unique to this system is its versatility for images and videos, and its real time capability, making it a suitable choice forpracticaluse in social media,journalism,andlawenforcementcombating the spread of misinformation with a guarantee of digital media authenticity.