Fourier Image Watermarking: Print-Cam Application

Digital image watermarking is an active research field since it provides protection, security, and authenticity of data. This paper presents development and implementation of a blind and robust watermarking application for ID images under a print-cam system. In the present case, the images are watermarked and printed on ID cards and then detected freehandedly with a smartphone camera. In order to design an efficient and robust image watermarking application, the attacks produced in print-cam processes, such as geometric distortions, must be resolved. Accordingly, the proposed watermarking approach is applied in the Fourier domain. Then, a frame-based projective rectification is integrated to deal with geometric distortions by using detection of Hough lines. Moreover, better robustness against print-cam watermarking attacks was achieved compared with the existing methods, and an Android application was designed and implemented based on the proposed scheme.

Efficient DNA Cryptographic Framework for Secured Data Encryption based on Chaotic Sequences

Data is big, data is diverse, data comes in zillion formats, it is important to ensure the safety and security of the shared data. With existing systems limited and evolving, the objective of the current research work is to develop a robust Image Encryption technique that is adept and effective at handling heterogeneous data and can withstand state-of-the-art hacking efforts such as brute force attacks, cropping attacks, mathematical attacks, and differential attacks. The proposed Efficient DNA Cryptographic System (EDCS) model presents a pseudorandom substitution method using logistic sine cosine chaotic maps, wherein there is very little correlation between adjacent pixels, and it can decode the image with or without noise, thereby making the proposed system noise-agnostic. The proposed EDCS-based Image model using Chaotic Maps showed enhancements in parameters such as Unified Average Changing Intensity (UACI), Number of Pixels Change Rate (NPCR), Histogram, and Entropy when compared with existing image security methods.

Automated Diagnostics: Advances in the Diagnosis of Intestinal Parasitic Infections in Humans and Animals

The increasingly close proximity between people and animals is of great concern for public health, given the risk of exposure to infectious diseases transmitted through animals, which are carriers of more than 60 zoonotic agents. These diseases, which are included in the list of Neglected Tropical Diseases, cause losses in countries with tropical and subtropical climates, and in regions with temperate climates. Indeed, they affect more than a billion people around the world, a large proportion of which are infected by one or more parasitic helminths, causing annual losses of billions of dollars. Several studies are being conducted in search for differentiated, more sensitive diagnostics with fewer errors. These studies, which involve the automated examination of intestinal parasites, still face challenges that must be overcome in order to ensure the proper identification of parasites. This includes a protocol that allows for elimination of most of the debris in samples, satisfactory staining of parasite structures, and a robust image database. Our objective here is therefore to offer a critical description of the techniques currently in use for the automated diagnosis of intestinal parasites in fecal samples, as well as advances in these techniques.