Detecting Deepfake Images: A Deep Learning Approach with Streamlit Integration

According to sophisticated machine learning algorithms, deep fake technology can produce incredibly lifelike fake images and videos that may be exploited for fraud, disinformation, and public opinion manipulation. This has raised serious concerns about the technology. In response to this emerging threat, this research project proposes a deep fake detection system utilizing convolutional neural networks (CNNs) to distinguish between authentic and manipulated media. The project begins with the collection and preprocessing of a diverse dataset containing both real and fake images and videos. The dataset is then used to train a CNN model, consisting of multiple convolutional and pooling layers followed by fully connected layers, to learn discriminative features that can differentiate between genuine and manipulated media. Experimental results demonstrate the effectiveness of the proposed deep fake detection system in accurately identifying fake content with high precision, recall, and accuracy. Additionally, the system is capable of processing both individual images and video streams, enabling real-time detection of deep fake content. Overall, this research contributes to the ongoing efforts to combat the proliferation of deep fake technology by providing a robust and reliable solution for detecting manipulated media, thereby safeguarding the integrity of digital content and mitigating the potential negative consequences of deep fake misuse.