Deep fake video/image detection using deep learning

With the widespread of deep fake technology, the potential to detect manipulated images has become an insistent concern. This study investigates the application of machine learning concept and its techniques, precisely CNNs (Convolutional-Neural-Networks) and LSTM (Long-Short-Term-Memory) networks to rectify deep fake images. CNNs are utilized for their strength in feature extraction from images capturing spatial hierarchies in data, while LSTMs are employed to understand the temporal dependencies that might exist in sequential frames of manipulated videos. The proposed theory combines these two architectures to harness their complementary strengths, delivering a powerful solution for detecting deep fakes. This proposed model showcases the efficiency of this hybrid approach, highlighting its potential in distinguishing between genuine and manipulated images with high accuracy. This research contributes to the development of reliable automated systems capable of mitigating the risks posed by deep fake technology. Our proposed model is trained and evaluated on a comprehensive dataset comprising both real and deep fake images with rigorous preprocessing and data augmentation techniques applied to enhance model robustness. The integration of CNN and LSTM networks leverages the strengths of both architectures enabling the model to achieve high accuracy in detecting deep fake images. Trained results demonstrate that our approach markedly improves detection rates over conventional methods, offering a dependable solution for the risks posed by deep fake technology.