Fake Image Detection System Using Convolutional Neural Networks

Abstract

The spread of fake images on digital platforms is a growing concern, affecting public perception, decision-making, and digital trust. With advanced image editing tools, manipulated images can be easily created and disseminated, making detection increasingly challenging. This study proposes a Fake Image Detection System using Convolutional Neural Networks (CNN) to classify images as real or fake. A dataset of authentic and manipulated images was collected and preprocessed through normalization, resizing, and augmentation. The CNN model comprising convolutional, max-pooling, and fully connected layers was trained and tested using a 70:30 ratio, resulting in 94.90% accuracy. This system has significant cybersecurity implications by reducing visual-based threats such as phishing, deepfake scams, and disinformation campaigns. By identifying tampered content early, it strengthens digital trust and safeguards public and private sector information ecosystems. In environments where visual credibility is critical such as journalism, national security, and law enforcement such a system can act as a preventive layer against the spread of manipulated media. Moreover, the model’s commercialization potential is notable. It can be integrated into content verification tools for media platforms, browser plugins, forensic applications, and enterprise cybersecurity solutions. Its scalability and adaptability make it suitable for both real-time analysis and backend forensic validation. Future work should emphasize real-time detection, integration with online platforms, and adaptability to evolving manipulation methods. Overall, the proposed system represents a crucial step toward protecting the authenticity of visual content and building user trust in digital environments.