Copy-Move Image Forgery Detection (CMFD) using Hybrid Approach
DOI:
https://doi.org/10.15662/IJEETR.2026.0802117Keywords:
Copy-move forgery, CenSurE, CNN, image processing, hybrid model, feature detectionAbstract
Copy-move image forgery is a common type of image manipulation where a part of an image is copied and pasted into another area of the same image, making it difficult to detect fake content. Detecting such forgeries is important in areas like digital forensics, security, and media verification. This project proposes a hybrid approach for detecting copy-move image forgery using CenSurE keypoint detection and Convolutional Neural Network (CNN). The CenSurE algorithm is used to detect keypoints and extract important features from the image efficiently, helping to identify duplicated regions.After feature extraction, a CNN model is used to classify and confirm whether the image is original or forged. The combination of CenSurE and CNN improves both detection speed and accuracy compared to using a single method. The system is tested using a dataset of original and tampered images, and the results show improved performance in terms of accuracy and reliability. This work proves that hybrid techniques are effective for real-world image forgery detection
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