Source camera identification using GLCM

Digital images are becoming main focus of work for the researchers. Digital image forensics (DIF) is at the forefront of security techniques, aiming to restore the lost trust in digital imagery by uncovering digital counterfeiting techniques. Source camera identification provides different ways to identify the characteristics of the digital devices used. Study of these techniques has been done as literature survey work; from this sensor imperfection based technique is chosen. Sensor pattern noise (SPN), carries abundance of information along with wide frequency range allows for reliable identification in the presence of many imaging sensors. Our proposed system consists of a novel technique used for extracting sensor noise from the database images, and then the feature extraction method is applied to extract the features. The model used for extracting sensor noise consists of use of Gradient based operators and Laplacian operators, a hybrid system consisting of best results from the above two operators obtain a third image giving the edges and noise present in it. The edges are removed by applying threshold to get the noise present in the image. This noisy image is then provided to the feature extraction module consisting of Gray level Co-occurrence Matrix (GLCM). It is used to extract various features based on its properties such as Homogeneity, Contrast, Correlation, and Entropy. The extracted features are used to do the performance evaluation based on various parameters. The accuracy parameter will give the matching rate for the entire dataset. The Sensor Pattern Noise (SPN) is extracted in the GLCM features and used for matching with the test set to get the exact match. The hybrid system used for SPN extraction along with the GLCM feature extraction yields better results.