A Multimodal Deepfake Detection System for Image, Audio, and Video Forensics Using Deep Learning and Spectral-Temporal Analysis

The rapid advancement of generative artificial intelligence, including Generative Adversarial Networks (GANs), diffusion models, and neural voice synthesis, has made it increasingly easy to generate hyper-realistic manipulated media known as deepfakes. Unchecked circulation of falsified images, cloned voice recordings, and face-swapped videos poses severe risks to digital media integrity, biometric security, and public trust. Traditional single-modality forensic detectors often fail when confronted with cross-modal manipulations where either the visual stream, the audio track, or both are synthetically altered. This paper presents an integrated multimodal deepfake detection system capable of authenticating digital images, standalone audio recordings, and audio-visual video streams within a unified framework. The proposed system is developed in Python using PyTorch and OpenCV for visual forensics and Librosa for acoustic signal analysis. Spatial facial artifacts in images and video frames are evaluated using depthwise separable convolutional neural networks (XceptionNet and MesoNet architectures), while inter-frame temporal inconsistencies are captured across sequential video frames. Simultaneously, audio streams are transformed into Mel-Frequency Cepstral Coefficients (MFCCs) and log-Mel spectrograms to expose synthetic speech and neural vocoder artifacts. A confidence-weighted fusion module combines modality-specific scores and generates Grad-CAM explainability heatmaps. Experimental testing demonstrates accurate forgery detection and smooth user interaction across image, audio, and video modalities.