Image content dependent semi-fragile watermarking with localized tamper detection

Content-independent watermarks and block-wise independency can be considered as vulnerabilities in semi-fragile watermarking methods. In this paper to achieve the objectives of semi-fragile watermarking techniques, a method is proposed to not have the mentioned shortcomings. In the proposed method, the watermark is generated by relying on image content and a key. Furthermore, the embedding scheme causes the watermarked blocks to become dependent on each other, using a key. In the embedding phase, the image is partitioned into non-overlapping blocks. In order to detect and separate the different types of attacks more precisely, the proposed method embeds three copies of each watermark bit into LWT coefficients of each 4x4 block. In the authentication phase, by voting between the extracted bits the error maps are created; these maps indicate image authenticity and reveal the modified regions. Also, in order to automate the authentication, the images are classified into four categories using seven features. Classification accuracy in the experiments is 97.97 percent. It is noted that our experiments demonstrate that the proposed method is robust against JPEG compression and is competitive with a state-of-the-art semi-fragile watermarking method, in terms of robustness and semi-fragility.

Full Quaternion Representation of Color images: A Case Study on QSVD-based Color Image Compression

For many years, channels of a color image have been processed individually, or the image has been converted to grayscale one with respect to color image processing. Pure quaternion representation of color images solves this issue as it allows images to be processed in a holistic space. Nevertheless, it brings additional costs due to the extra fourth dimension. In this paper, we propose an approach for representing color images with full quaternion numbers that enables us to process color images holistically without additional cost in time, space and computation. With taking auto- and cross-correlation of color channels into account, an autoencoder neural network is used to generate a global model for transforming a color image into a full quaternion matrix. To evaluate the model, we use UCID dataset, and the results indicate that the model has an acceptable performance on color images. Moreover, we propose a compression method based on the generated model and QSVD as a case study. The method is compared with the same compression method using pure quaternion representation and is assessed with UCID dataset. The results demonstrate that the compression method using the proposed full quaternion representation fares better than the other in terms of time, quality, and size of compressed files.