Project Title: AI Image Forgery Detection with a Cybersecurity Shield
Description:
The rapid advancement of Artificial Intelligence (AI) has significantly enhanced the ability to generate highly realistic synthetic images, creating serious challenges for cybersecurity, digital forensics, and media authenticity verification. As AI-generated images become increasingly convincing, distinguishing manipulated content from authentic images has become a complex task that requires intelligent and reliable detection techniques.
This research presents an advanced AI-powered image forgery detection framework that combines customized transfer learning with deep learning to accurately identify forged facial images. Unlike conventional approaches that directly employ pretrained convolutional neural networks, the proposed framework customizes the EfficientNet architecture by replacing the original ImageNet classification head with a task-specific binary classifier and selectively fine-tuning higher network layers to enhance forgery-specific feature learning while preserving the generalized visual knowledge acquired during pretraining.
To improve model robustness and generalization capability, a comprehensive preprocessing pipeline incorporating facial image preprocessing and data augmentation techniques is employed. The training process is further optimized using the AdamW optimizer, adaptive learning rate scheduling, and Early Stopping to accelerate convergence, prevent overfitting, and improve overall predictive performance. The best-performing model is automatically selected according to validation performance before being evaluated on a completely independent testing dataset to ensure scientifically reliable performance estimation.
Beyond achieving high classification accuracy, the proposed framework has been deployed as a Flask-based web application, enabling practical real-time image authenticity verification through an intuitive user interface. Furthermore, Explainable Artificial Intelligence (XAI) techniques are incorporated to provide visual interpretation of the model's predictions, improving transparency and user confidence during decision-making.
The proposed framework offers an effective combination of accuracy, computational efficiency, scalability, and practical applicability, making it a promising solution for cybersecurity systems, digital forensic investigations, media authentication, and intelligent digital content protection against emerging AI-generated image manipulation techniques.
Uploaded At: 2026-07-05 06:55:32