Robust image data hiding method based on multiple backups and pixel bit weight

Digital images are easily corrupted by attacks during transmission and most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, we propose a robust image data hiding method based on multiple backups and pixel bit weight (PBW). Especially multiple backups of every pixel bit are pre-embedded into a cover image according to a reference matrix. Since different pixel bits have different weights, the most significant bits (MSBs) occupy more weights on the secret image than those of the least significant bits (LSBs). Accordingly, some backups of LSBs are substituted by the MSBs to increase the backups of MSBs so that the quality of the extracted secret image can be improved. Experimental results show that the proposed algorithm is robust to cropping and noise attacks for secret image.

Antitamper Image Watermarking Based on Cellular Network Topology for IoT-Themed Mobile Forensics

The Internet of Things (IoT) connects physical and digital worlds with mobile devices, accompanied by a surge in cybersecurity issues. With the rapid adoption of mobile devices, mobile forensics emerges as a new interdisciplinary field that concerns many forms of sabotage and cybercrime in the context of mobile computing. One of the most common cyberattacks is tampering. Digital watermarking is a tamper-evident technique used to protect data integrity. In this paper, we present an antitamper image watermarking scheme designed for mobile communications with low computational cost. A reference matrix based on cellular network topology is introduced to guide the watermark embedding and extraction processes. This reference matrix serves as a lookup table to reduce computational complexity, thereby enabling efficient implementation on mobile devices. Our scheme is aimed at offering high accuracy in detecting and localizing tampered regions. We also achieve a high watermarking capacity while leaving the visual quality of the carrier images nearly unharmed. Experimental results validate the effectiveness of our scheme against various types of simulated forgery including cropping and copy/paste attacks.

Analytical Performance of a Standardized Kit for Mass Spectrometry-based Measurements of Human Glycosaminoglycans

Glycosaminoglycans (GAGs) are long linear sulfated polysaccharides implicated in processes linked to disease development such as mucopolysaccharidosis, respiratory failure, cancer, and viral infections, thereby serving as potential biomarkers. A successful clinical translation of GAGs as biomarkers depends on the availability of standardized GAG measurements. However, owing to the analytical complexity associated with the quantification of GAG concentration and structural composition, a standardized method to simultaneously measure multiple GAGs is missing. In this study, we sought to characterize the analytical performance of a ultra-high-performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry (UHPLC-MS/MS)-based kit for the quantification of 17 GAG disaccharides. The kit showed acceptable linearity, selectivity and specificity, accuracy and precision, and analyte stability in the absolute quantification of 15 GAG disaccharides. In native human samples, here using urine as a reference matrix, the analytical performance of the kit was acceptable for the quantification of CS disaccharides. Intra- and inter-laboratory tests performed in an external laboratory demonstrated robust reproducibility of GAG measurements showing that the kit was acceptably standardized. In conclusion, these results indicated that the UHPLC-MS/MS kit was standardized for the simultaneous measurement of GAG disaccharides allowing for comparability of measurements and enabling translational research.SummaryAnalytical performance of a kit for standardized GAG measurements, based on an established UHPLC-MS/MS method

Characterization of the Tumor Microenvironment of Neurofibromatosis Type I Plexiform Neurofibromas

Background/Objective: Neurofibromatosis type 1 (NF1) is a cancer predisposition syndrome caused by mutations in the NF1 tumor suppressor gene. Patients with NF1 develop tumors of the peripheral nervous system called plexiform neurofibromas (PNs). These histopathologically complex tumors are composed of various immune and inflammatory cells. Mast cells have previously been identified as one key immune cell lineage underpinning PN initiation and progression, however new technologies leveraging RNA-sequencing (RNAseq) allow for the broad and systematic characterization of the PN tumor microenvironment. Here we utilized these tools to delineate PN cellular composition. Methods: RNA seq was performed on murine wild type (n=6) and PN (n=6) tissues. We utilized CIBERSORT to profile the cellular constituents of the PN microenvironment. CIBERSORT is a deconvolution method that uses a reference matrix to estimate the relative proportions of various cell types. Statistical analyses were performed on cell lineage subtypes delineated by CIBERSORT. We further performed a Gene Set Enrichment Analysis (GSEA) to identify which pathways and cytokines might be upregulated in PNs. Results: Using a murine reference matrix, the macrophage lineage, M0 (p = 0.072), M1 (p = 0.1), were upregulated in PNs (n=6) compared to WT (n=6). A human reference matrix showed M2 (p=0.025) to be upregulated in PNs. GSEA showed IL-1, IL-6, IL-8, TNF and Type I IFN and cytokine secretion to be upregulated in PNs compared to WT. Conclusion: Macrophages were among the most upregulated components of the NF1 tumor microenvironment and upregulation of IL-1, IL-6, IL-8, TNF and Type I IFN production may be contributing to inflammation that is critical in the initiation and progression of PNs. Scientific/Clinical/Policy Impact and Implications: Pharmacotherapies that can target the macrophage lineage and/or aforementioned cytokines may have utility in the treatment of PNs. Further studies are necessary to evaluate this hypothesis.

Robust data hiding with multiple backups and optimized reference matrix

Most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, a robust data hiding with multiple backups and optimized reference matrix is proposed in this paper. Specifically, secret data is divided into a set of groups and multiple backups of each group data are generated according to the number of backups. The cover image is divided into several blocks. A reference matrix is constructed by four constraints to assist data hiding and data extraction. The proposed method aims to extract exactly at least one backup of each group data so that the correct backups can construct the secret data well if the stego-image is corrupted. Experimental results show that the proposed algorithm is robust to cropping and noise attacks.

Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment

Facial age estimation is of interest due to its potential to be applied in many real-life situations. However, recent age estimation efforts do not consider juveniles. Consequently, we introduce a juvenile age detection scheme called LaGMO, which focuses on the juvenile aging cues of facial shape and appearance. LaGMO is a combination of facial landmark points and Term Frequency Inverse Gravity Moment (TF-IGM). Inspired by the formation of words from morphemes, we obtained facial appearance features comprising facial shape and wrinkle texture and represented them as terms that described the age of the face. By leveraging the implicit ordinal relationship between the frequencies of the terms in the face, TF-IGM was used to compute the weights of the terms. From these weights, we built a matrix that corresponds to the possibilities of the face belonging to the age. Next, we reduced the reference matrix according to the juvenile age range (0–17 years) and avoided the exhaustive search through the entire training set. LaGMO detects the age by the projection of an unlabeled face image onto the reference matrix; the value of the projection depicts the higher probability of the image belonging to the age. With Mean Absolute Error (MAE) of 89% on the Face and Gesture Recognition Research Network (FG-NET) dataset, our proposal demonstrated superior performance in juvenile age estimation.