Utilization of HOG-SVD based Features with Connected Component Labeling for Multiple Copy-move Image Forgery Detection

Copy-move forgery is one of the most regarded image forgery technique to tamper information conveyed by the image. In this technique, segment of original image is replicated and pasted across the same image to produce forged image. This technique is capable to hide selective information or to add fictitious details in image. Detection of this form of forgery is one of the significant area of information security. In this paper, we propose block-based approach for copy-move image forgery detection to secure information conveyed through the image by identifying the forged images and to prevent spreading of tampered subject matter. Proposed model divides suspicious image in overlapping blocks. We extracted block features using Histogram of Oriented Gradients (HOG) and Singular Value Decomposition (SVD). Lexicographical sorting is performed over feature matrix followed by Euclidean distance computation to recognize similar feature vectors. To remove false match detection, Connected component labeling is utilized. Our scheme achieves highest F-measure than former techniques, when forged image sustain plain multiple copy-move, multiple copy-move with contrast adjustment, color reduction, and image blurring attacks.