Abstract: Deep fake technology poses significant threats to the authenticity of digital media, leading to misinformation, reputational damage, and security risks. The ability to manipulate videos and images with AI has resulted in concerns over trustworthiness in media, cyber threats, and fraudulent activities. Traditional detection methods, including manual inspection and rule-based algorithms, have proven inadequate in identifying these rapidly evolving deep fake techniques. This project introduces a deep learning-based solution utilizing Convolutional Neural Networks (CNNs) for detailed image analysis and Recurrent Neural Networks (RNNs) for detecting temporal inconsistencies in videos. The system integrates attention mechanisms to focus on subtle artifacts and adversarial training to enhance detection robustness. Additionally, it continuously learns from new deep fake patterns, ensuring adaptability against emerging manipulation techniques. Designed for scalability and real-time performance, our system is optimized to run efficiently on standard hardware while achieving high accuracy and low false-positive rates. By providing a reliable tool for deep fake detection, this project contributes to media integrity and cybersecurity
Keywords: Deep Fake, Deep Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Adversarial Training, Image Forgery Detection, Real- Time Detection, Artificial Intelligence (AI).
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DOI:
10.17148/IJARCCE.2025.14370
