Feature based Image Authentication using Symmetric Surround Saliency Mapping in Image Forensics

Abstract

an efficient image security, image hashing is one of the solutions for image authentication. A robust image hashing mechanism must be robust to image processing operations as well as geometric distortions. A better hashing technique must ensure an efficient detection of image forgery like insertion, deletion, replacement of objects, malicious color tampering, and for locating the exact forged areas. This paper describes a novel image hash function, which is generated by using both global and local features of an image. The global features are the representation of Zernike moments on behalf of luminance and chrominance components of the image as a whole. The local features include texture information as well as position of significant regions of the image. The secret keys can be introduced into the system, in places like feature extraction and hash formulation to encrypt the hash. The hash incorporation into the system is found very sensitive to abnormal image modifications and hence robust to splicing and copy-move type of image tampering and, therefore, can be applicable to image authentication. As in the generic system, the hashes of the reference and test images are compared by finding the hamming or hash distance. By setting the thresholds with the distance, the received image can be stated as authentic or non-authentic. And finally location of forged regions and type of forgery are found by decomposing the hashes. Compared to most recent work done in this area, our algorithm is simple and cost effective with better scope of security. Keywordsmoments, Forgery detection, SHA-1, MD5 Image hash, Salient detection