Secure and Robust Fragile Watermarking Scheme for Medical Images

Over the past decade advances in computer-based communication and health services, the need for image security becomes urgent to address the requirements of both safety and non-safety in medical applications. This paper proposes a new fragile watermarking based scheme for image authentication and self-recovery for medical applications. The proposed scheme locates image tampering as well as recovers the original image. A host image is broken into 4×4 blocks and Singular Value Decomposition (SVD) is applied by inserting the traces of block wise SVD into the Least Significant Bit (LSB) of the image pixels to figure out the transformation in the original image. Two authentication bits namely block authentication and self-recovery bits were used to survive the vector quantization attack. The insertion of self-recovery bits is determined with Arnold transformation, which recovers the original image even after a high tampering rate. SVD-based watermarking information improves the image authentication and provides a way to detect different attacked area. The proposed scheme is tested against different types of attacks such are text removal attack, text insertion attack, and copy and paste attack

Logistic Map-Based Fragile Watermarking for Pixel Level Tamper Detection and Resistance

An efficient fragile image watermarking technique for pixel level tamper detection and resistance is proposed. It uses five most significant bits of the pixels to generate watermark bits and embeds them in the three least significant bits. The proposed technique uses a logistic map and takes advantage of its sensitivity property to a small change in the initial condition. At the same time, it incorporates the confusion/diffusion and hashing techniques used in many cryptographic systems to resist tampering at pixel level as well as at block level. This paper also presents two new approaches called nonaggressive and aggressive tamper detection algorithms. Simulations show that the proposed technique can provide more than 99.39% tamper detection capability with less than 2.31% false-positive detection and less than 0.61% false-negative detection responses

Robust Image Hashing Using Radon Transform and Invariant Features

A robust image hashing method based on radon transform and invariant features is proposed for image authentication, image retrieval, and image detection. Specifically, an input image is firstly converted into a counterpart with a normalized size. Then the invariant centroid algorithm is applied to obtain the invariant feature point and the surrounding circular area, and the radon transform is employed to acquire the mapping coefficient matrix of the area. Finally, the hashing sequence is generated by combining the feature vectors and the invariant moments calculated from the coefficient matrix. Experimental results show that this method not only can resist against the normal image processing operations, but also some geometric distortions. Comparisons of receiver operating characteristic (ROC) curve indicate that the proposed method outperforms some existing methods in classification between perceptual robustness and discrimination