Counterfeit IC Detection System

Counterfeit ICs are a growing concern in the electronics industry, leading to financial losses and compromised security. This project implements a deep learning-based counterfeit IC detection system, leveraging Convolutional Neural Networks (CNNs) to analyze visual patterns in integrated circuits (ICs). Traditional manual inspection techniques fail to detect subtle counterfeit variations, making AI-driven solutions a necessity.

To improve classification accuracy, the dataset of 85 images was expanded to 170 using image augmentation techniques such as rotation, resizing, and edge enhancement using the Hough-Lines algorithm. The CNN architecture consisted of three hidden layers with 64, 32, and 256 nodes, trained using the Adam optimizer with early stopping to prevent overfitting. The model was evaluated under different configurations, achieving 93.2% accuracy when applying image augmentation and edge detection methods. Confusion matrices validated the reduction in false positives and false negatives, making this approach highly reliable for real-world counterfeit detection.

This project highlights the practical application of deep learning in hardware security and supply chain integrity. Future improvements may include dataset expansion with diverse IC types, fine-tuning hyperparameters for higher accuracy, and deploying the model for real-time classification on manufacturing lines. By leveraging AI in counterfeit detection, this approach significantly enhances electronics quality control and security assurance.