Deepfake Image Detection Using Explainable Ai And Deep Learning

Deepfake technology has grown exponentially, and it has changed our perspective of managing digital content. The technology has many applications, but its ease of access has brought many risks. These risks include identity theft, misinformation spread, development of non-consensual content, and a decrease in trust in media. To address these challenges, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for deepfake image detection. We developed a CNN model and four hybrid CNN-LSTM models in our study. Our approach integrates the strengths of CNNs for spatial feature extraction and LSTMs for pattern recognition in features, offering a robust and adaptable detection mechanism. The results show that the CNN-LSTM hybrid model performs very well in detecting deepfake images with high accuracy. In comparison to existing works, our model demonstrates superior accuracy and robustness. Unlike other studies, we validated the performance of all models using k-fold cross validation and incorporated SHAP analysis to provide interpretability, identifying critical image features contributing to predictions. This transparency enhances the model's reliability and usability in real-world applications. Our findings highlight the flexibility and effectiveness of the hybrid CNN-LSTM model, which outperforms traditional approaches in detecting deepfakes. By addressing various deepfake techniques and ensuring adaptability to evolving manipulations, the proposed model represents a significant advancement in cybersecurity and the fight against malicious use of AI technologies.