| dc.description.abstract | Recent years have witnessed an exponential growth in the use of digital images due to development of high quality digital cameras and multimedia technology. Easy availability of user-friendly image editing software has made modification of images a popular child’s play. In this environment, the integrity of an image cannot be taken for granted. Malicious tampering has serious ramification in legal documents, copyright issues, photojournalism, celebrities’ lifestyles, fashion statements, beauty and fitness products, entertainment sector, medical science, biometric images and forensic cases. The proposed work is based on features based hash generation of a digital image and thereby detection and localization of image tampering that is done with malicious intention, no matter howsoever small the tampering is. The hashing algorithm generates a short binary string by extracting the feature vectors of the image and mapping them into robust hash values. This hash mapping meets the two requirements of being sensitive to tampering in the image and being robust against content preserving manipulations. The first part of the work uses correlation coefficient to generate a hash representation of the image which is then utilized for tampering detection. This method is extended later to use properties of Singular Value Decomposition (SVD) to derive the hash values. SVD enables us to generate key based hash values. We show that this hash can be used in secured transmission of image information on the public network. We emphasize that the malicious tampering is generally done on the structural part of the image. Therefore, in the third part of our study, we have used Canny Edge Detector to extract the features of the image by detecting the edges present in it. Having the edges identified, tampering detection and localization are carried out. This method of tampering detection and localization has been found to be promising. The last part of the thesis discusses Comprehensive Image Index which is able to detect multiple types of tampering, viz., brightness, contrast, and structural, simultaneously. We observe that a structural tampering is always followed by brightness and contrast changes in the image. We also establish that even brightness or contrast or both change can be seen as a malicious tampering if exceeds a threshold value. The sensitivity of the technique and its robustness have been discussed quantitatively for each method. | |
