Satar Shaker Muhammad Adaptive deep learning framework for detecting fake faces Directorate General of Education in Thi-Qar Governorate, Thi-Qar, Iraq
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Abstract
The rapid development of face generation technologies in recent years has enabled the creation of stunningly realistic human face images, effectively blurring the lines between authentic and synthetic content. While these technologies offer positive applications in education, entertainment, and digital art, their potential for misuse—particularly on social media—is profound. The proliferation of deep-fakes poses a significant threat to digital trust, facilitating electronic fraud and identity manipulation, which necessitates the development of effective detection solutions. This paper addresses this escalating threat by proposing an adaptive detection framework based on pre-trained Convolutional Neural Networks (CNNs). Unlike traditional methods that struggle with unseen data and environmental noise, our approach optimizes ResNet50, DenseNet201, and Alex Net architectures through customized data preprocessing and hyper parameter tuning. Evaluated on the rvf10k dataset—selected for its diverse illumination and ethnic representation—the proposed model achieves a peak accuracy of 99.89%. Furthermore, this work provides a comprehensive analysis of computational cost and inference speed, demonstrating robustness against over-fitting via cross-validation techniques. The results confirm that the proposed framework significantly enhances detection reliability and contributes to securing digital content integrity.
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