Detection of image manipulation using Feature-Based Algorithms

In today’s digital age, the integrity of images is paramount as they serve various critical purposes, from news reporting to personal communication and marketing. With the widespread availability of powerful image editing tools, the risk of image manipulation, particularly copy and move forgery, has increased. This deceitful practice involves copying or moving objects within images to create manipulated versions, potentially deceiving viewers by altering the context or composition. To address this challenge, this research project is dedicated to image forgery detection with a specific focus on identifying copy and move forgery. We propose a comprehensive solution that leverages digital image processing techniques, encompassing image preprocessing, feature point extraction, feature matching, and the clustering of similar features. Our approach incorporates advanced algorithms, such as the Stationary Wavelet Transform (SWT) for image preprocessing, the Scale-Invariant Feature Transform (SIFT) for feature point extraction, and the Geometric Two Nearest Neighbour (G2NN) for feature matching. Additionally, we employ hierarchical clustering to create clusters of similar feature points, enabling us to pinpoint potential forgery regions. In summary, the aim to provide a robust and effective method for detecting and mitigating image manipulation, with a particular focus on copy and move forgery. By achieving accurate detection of such manipulations, we contribute to preserving the authenticity and integrity of digital images, thereby protecting against misleading and deceptive uses of altered images.