Explainability of AI in Multimedia Security

Multimedia security systems have been enabled with artificial intelligence (AI) technology, which has made these systems better protect sensitive data within images, video, and biometric data. The black-box nature of such an AI model, particularly the deep learning system, poses considerable challenges in trust, transparency, and accountability in its many applications, critical deepfake detection, and biometric authentication. This research explores how Explainable AI (XAI) can address these challenges in making AI models more interpretable and transparent. Based on this, it develops user trust in AI-based security systems by considering XAI techniques such as Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Gradient-Weighted Class Activation Mapping (Grad-CAM). Second, two case studies on using XAI to improve the models and make the system more transparent through deepfake detection with Xception-Net and biometric authentication using Support Vector Machine (SVM) are portrayed here. It gives the AI model a basis for accuracy and creates a positive environment for trust in the model. Finally, the paper presents future research directions, such as optimizing XAI techniques for use within real-time applications and standardizing evaluation metrics for multimedia security.