Detection of Image Splicing Using CNN

In the era of advanced digital imaging and widespread multimedia sharing, image forgery has become an increasingly significant concern. Among various types of image manipulations, splicing — where regions from multiple images are combined to create a forged image — is one of the most common and deceptive forms. Detecting such manipulations is critical for applications in digital forensics, media verification, and security. This paper proposes an effective approach for the detection of image splicing using Convolutional Neural Networks (CNN). The proposed model leverages the ability of CNNs to automatically learn hierarchical feature representations from raw image data, enabling robust discrimination between authentic and spliced images. A carefully curated dataset containing both authentic and spliced images was used to train and evaluate the model. Experimental results demonstrate that the proposed CNN-based method achieves high accuracy in detecting image splicing, outperforming several traditional image forensic techniques. The study highlights the potential of deep learning-based solutions in addressing complex image forensics challenges and underscores the importance of integrating AI-driven tools in digital content authentication systems.