View invariant DIBR-3D image watermarking using DT-CWT

In 3D image compression, depth image based rendering (DIBR) is one of the latest techniques where the center image (say the main view, is used to synthesise the left and the right view image) and the depth image are communicated to the receiver side. It has been observed in the literature that most of the existing 3D image watermarking schemes are not resilient to the view synthesis process used in the DIBR technique. In this paper, a 3D image watermarking scheme is proposed which is invariant to the DIBR view synthesis process. In this proposed scheme, 2D-dual-tree complex wavelet transform (2D-DT-CWT) coefficients of centre view are used for watermark embedding such that shift invariance and directional property of the DT-CWT can be exploited to make the scheme robust against view synthesis process. A comprehensive set of experiments has been carried out to justify the robustness of the proposed scheme over the related existing schemes with respect to the JPEG compression and synthesis view attack

Ownership protection of plenoptic images by robust and reversible watermarking

Plenoptic images are highly demanded for 3D representation of broad scenes. Contrary to the images captured by conventional cameras, plenoptic images carry a considerable amount of angular information, which is very appealing for 3D reconstruction and display of the scene. Plenoptic images are gaining increasing importance in areas like medical imaging, manufacturing control, metrology, or even entertainment business. Thus, the adaptation and refinement of watermarking techniques to plenoptic images is a matter of raising interest. In this paper a new method for plenoptic image watermarking is proposed. A secret key is used to specify the location of logo insertion. Employing discrete cosine transform (DCT) and singular value decomposition (SVD), a robust feature is extracted to carry the watermark. The Peak Signal to Noise Ratio (PSNR) of the watermarked image is always higher than 54.75 dB which is by far more than enough for Human Visual System (HVS) to discriminate the watermarked image. The proposed method is fully reversible and, if no attack occurs, the embedded logo can be extracted perfectly even with the lowest figures of watermark strength. Even if enormous attacks occur, such as Gaussian noise, JPEG compression and median filtering, our method exhibits significant robustness, demonstrated by promising bit error rate (BER) performance

Information hiding and copyrights

This chapter explores the use of steganography on digital files and produces an enhanced technique that addresses the major vulnerabilities that make algorithms less reliable in securing data. Through a review of historical techniques in the field, the study identifies weaknesses in the algorithms to improve security and increase capacity using different techniques. One of the approaches proposed in this study involves a distributed method, which is simple, clear, low-cost, and agile. The study also analyses data manipulation and embedding processes in different files and for different purposes, such as vulnerabilities or placeholders exploited by criminals distributing viruses over the internet using Steganography. The results of the study can help forensic analysts identify secret content and raise awareness about protecting against eavesdropping data on devices. The study proposes a new scheme to improve Steganography called DSoBMP, together with guideline materials that have been published in four international peer-reviewed journals, including Springer and used as a stepping stone to collaborate in a worldwide book publication

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas