Abstract: The increasing prevalence of AI-generated deepfake images has become a significant concern in the context of misinformation and digital security. Deepfake technology, driven by generative adversarial networks (GANs) and sophisticated AI algorithms, enables highly realistic image alterations, making it challenging to differentiate genuine visuals from manipulated ones. This study introduces a deepfake image detection system utilizing convolutional neural networks (CNNs) for classification. By employing deep learning techniques, the system evaluates the authenticity of images and identifies alterations with high precision. Through training on diverse datasets, the model aims to bolster media integrity and strengthen digital security. The findings underscore the importance of reliable deepfake detection in minimizing the risks of manipulated content, offering valuable applications in fields such as journalism, social media verification, and digital forensics.
Keywords: Deepfake detection, image authenticity, machine learning, CNN, digital security, media verification, digital forensics
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DOI:
10.17148/IJARCCE.2025.14623
