Deep Learning for Reversible Steganography: Principles and Insights

Deep-learning\textendash{centric} reversible steganography has emerged as a promising research paradigm. A direct way of applying deep learning to reversible steganography is to construct a pair of encoder and decoder, whose parameters are trained jointly, thereby learning the steganographic system as a whole. This end-to-end framework, however, falls short of the reversibility requirement because it is difficult for this kind of monolithic system, as a black box, to create or duplicate intricate reversible mechanisms. In response to this issue, a recent approach is to carve up the steganographic system and work on modules independently. In particular, neural networks are deployed in an analytics module to learn the data distribution, while an established mechanism is called upon to handle the remaining tasks. In this paper, we investigate the modular framework and deploy deep neural networks in a reversible steganographic scheme referred to as prediction-error modulation, in which an analytics module serves the purpose of pixel intensity prediction. The primary focus of this study is on deep-learning\textendash{based} context-aware pixel intensity prediction. We address the unsolved issues reported in related literature, including the impact of pixel initialisation on prediction accuracy and the influence of uncertainty propagation in dual-layer embedding. Furthermore, we establish a connection between context-aware pixel intensity prediction and low-level computer vision and analyse the performance of several advanced neural networks

Catalytic Mechanism Investigation of Lysine-Specific Demethylase 1 (LSD1): A Computational Study

Lysine-specific demethylase 1 (LSD1), the first identified histone demethylase, is a flavin-dependent amine oxidase which specifically demethylates mono- or dimethylated H3K4 and H3K9 via a redox process. It participates in a broad spectrum of biological processes and is of high importance in cell proliferation, adipogenesis, spermatogenesis, chromosome segregation and embryonic development. To date, as a potential drug target for discovering anti-tumor drugs, the medical significance of LSD1 has been greatly appreciated. However, the catalytic mechanism for the rate-limiting reductive half-reaction in demethylation remains controversial. By employing a combined computational approach including molecular modeling, molecular dynamics (MD) simulations and quantum mechanics/molecular mechanics (QM/MM) calculations, the catalytic mechanism of dimethylated H3K4 demethylation by LSD1 was characterized in details. The three-dimensional (3D) model of the complex was composed of LSD1, CoREST, and histone substrate. A 30-ns MD simulation of the model highlights the pivotal role of the conserved Tyr761 and lysine-water-flavin motif in properly orienting flavin adenine dinucleotide (FAD) with respect to substrate. The synergy of the two factors effectively stabilizes the catalytic environment and facilitated the demethylation reaction. On the basis of the reasonable consistence between simulation results and available mutagenesis data, QM/MM strategy was further employed to probe the catalytic mechanism of the reductive half-reaction in demethylation. The characteristics of the demethylation pathway determined by the potential energy surface and charge distribution analysis indicates that this reaction belongs to the direct hydride transfer mechanism. Our study provides insights into the LSD1 mechanism of reductive half-reaction in demethylation and has important implications for the discovery of regulators against LSD1 enzymes

Bloom filter based secure and anonymous DSR protocol in wireless ad hoc networks

Wireless ad hoc networks, especially in the hostile environment, are vulnerable to traffic analysis which allows the adversary to trace the routing messages and the sensitive data packets. Anonymity mechanism in ad hoc networks is a critical securing measure method employed to mitigate these problems. In this paper, we propose a novel secure and anonymous source routing protocol, called SADSR, based on Dynamic Source Routing (DSR) for wireless ad hoc networks. In the proposed scheme, we use the pseudonym, pseudonym based cryptography and the bloom filter to establish secure and anonymous routing in wireless ad hoc networks. Compared to other anonymous routing protocol, SADSR is not only anonymous but also the secure in the routing discover process and data transmission process

Exploration of the Contribution of Fire Carbon Emissions to PM2.5 and Their Influencing Factors in Laotian Tropical Rainforests

It is of great significance to understand the drivers of PM2.5 and fire carbon emission (FCE) and the relationship between them for the prevention, control, and policy formulation of severe PM2.5 exposure in areas where biomass burning is a major source. In this study, we considered northern Laos as the area of research, and we utilized space cluster analysis to present the spatial pattern of PM2.5 and FCE from 2003–2019. With the use of a random forest and structural equation model, we explored the relationship between PM2.5 and FCE and their drivers. The key results during the target period of the study were as follows: (1) the HH (high/high) clusters of PM2.5 concentration and FCE were very similar and distributed in the west of the study area; (2) compared with the contribution of climate variables, the contribution of FCE to PM2.5 was weak but statistically significant. The standardized coefficients were 0.5 for drought index, 0.32 for diurnal temperature range, and 0.22 for FCE; (3) climate factors are the main drivers of PM2.5 and FCE in northern Laos, among which drought and diurnal temperature range are the most influential factors. We believe that, as the heat intensifies driven by climate in tropical rainforests, this exploration and discovery can help regulators and researchers better integrate drought and diurnal temperature range into FCE and PM2.5 predictive models in order to develop effective measures to prevent and control air pollution in areas affected by biomass combustion

Deep learning for predictive analytics in reversible steganography

Deep learning is regarded as a promising solution for reversible steganography. There is an accelerating trend of representing a reversible steo-system by monolithic neural networks, which bypass intermediate operations in traditional pipelines of reversible steganography. This end-to-end paradigm, however, suffers from imperfect reversibility. By contrast, the modular paradigm that incorporates neural networks into modules of traditional pipelines can stably guarantee reversibility with mathematical explainability. Prediction-error modulation is a well-established reversible steganography pipeline for digital images. It consists of a predictive analytics module and a reversible coding module. Given that reversibility is governed independently by the coding module, we narrow our focus to the incorporation of neural networks into the analytics module, which serves the purpose of predicting pixel intensities and a pivotal role in determining capacity and imperceptibility. The objective of this study is to evaluate the impacts of different training configurations upon predictive accuracy of neural networks and provide practical insights. In particular, we investigate how different initialisation strategies for input images may affect the learning process and how different training strategies for dual-layer prediction respond to the problem of distributional shift. Furthermore, we compare steganographic performance of various model architectures with different loss functions

A TaSnRK1α Modulates TaPAP6L‐Mediated Wheat Cold Tolerance through Regulating Endogenous Jasmonic Acid

Abstract Here, a sucrose non‐fermenting‐1‐related protein kinase alpha subunit (TaSnRK1α‐1A) is identified as associated with cold stress through integration of genome‐wide association study, bulked segregant RNA sequencing, and virus‐induced gene silencing. It is confirmed that TaSnRK1α positively regulates cold tolerance by transgenes and ethyl methanesulfonate (EMS) mutants. A plastid‐lipid‐associated protein 6, chloroplastic‐like (TaPAP6L‐2B) strongly interacting with TaSnRK1α‐1A is screened. Molecular chaperone DJ‐1 family protein (TaDJ‐1‐7B) possibly bridged the interaction of TaSnRK1α‐1A and TaPAP6L‐2B. It is further revealed that TaSnRK1α‐1A phosphorylated TaPAP6L‐2B. Subsequently, a superior haplotype TaPAP6L‐2B30S/38S is identified and confirmed that both R30S and G38S are important phosphorylation sites that influence TaPAP6L‐2B in cold tolerance. Overexpression (OE) and EMS‐mutant lines verified TaPAP6L positively modulating cold tolerance. Furthermore, transcriptome sequencing revealed that TaPAP6L‐2B‐OE lines significantly increased jasmonic acid (JA) content, possibly by improving precursor α‐linolenic acid contributing to JA synthesis and by repressing JAR1 degrading JA. Exogenous JA significantly improved the cold tolerance of wheat plants. In summary, TaSnRK1α profoundly regulated cold stress, possibly through phosphorylating TaPAP6L to increase endogenous JA content of wheat plants