Deepfake Detection: A Multi-Algorithmic and Multi-Modal Approach for Robust Detection and Analysis

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Abstract

In a time when deepfakes are eroding the reliability of digital media, our innovative research introduces a multi-faceted framework that achieves unprecedented levels of detection accuracy. Boasting a 97% success rate in verifying visual content and an almost unblemished 98.5% in audio analysis, our system serves as a formidable barrier against the malicious alteration of digital assets. Central to our model's stellar performance is the seamless integration of convolutional neural networks (CNNs) with ReLU activation mechanisms, all fine-tuned via stochastic gradient descent (SGD). This expertly engineered architecture is highly proficient at analyzing the nuanced spatial features of visual media, and it works in synergy with cutting-edge machine learning algorithms. For the audio detection aspect, we employ random forest algorithms, celebrated for their robustness and versatility. This ensemble learning approach adds an extra layer of complexity to the model, effectively identifying the intricate spectral and temporal characteristics of audio streams, thereby boosting the overall efficacy of our detection system. Our methodology is further fortified by meticulous data preprocessing methods, such as normalization and data augmentation, which ensure the model's robustness against a myriad of deepfake techniques. This groundbreaking research not only establishes a new benchmark in the arena of deepfake detection but also has significant ramifications for the wider field of cybersecurity and the preservation of digital authenticity. With its unmatched performance metrics, our research represents a pivotal advancement in combating the growing menace of deepfakes in today's digital society.