StegNet: Mega Image Steganography Capacity with Deep Convolutional Network

Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only 0.76% of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We~further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.Comment: https://github.com/adamcavendish/StegNet-Mega-Image-Steganography-Capacity-with-Deep-Convolutional-Networ

Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model

Resource allocation is the process of optimizing the rare resources. In the area of security, how to allocate limited resources to protect a massive number of targets is especially challenging. This paper addresses this resource allocation issue by constructing a game theoretic model. A defender and an attacker are players and the interaction is formulated as a trade-off between protecting targets and consuming resources. The action cost which is a necessary role of consuming resource, is considered in the proposed model. Additionally, a bounded rational behavior model (Quantal Response, QR), which simulates a human attacker of the adversarial nature, is introduced to improve the proposed model. To validate the proposed model, we compare the different utility functions and resource allocation strategies. The comparison results suggest that the proposed resource allocation strategy performs better than others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference

Disentangling the Influence of Landscape Characteristics, Hydroclimatic Variability and Land Management on Surface Water NO3-N Dynamics : Spatially Distributed Modeling Over 30 yr in a Lowland Mixed Land Use Catchment

Funding Information: Songjun Wu is funded by the Chinese Scholarship Council (CSC). Contributions from Chris Soulsby were supported by the Leverhulme Trust through the ISO‐LAND project (grant no. RPG 2018 375). The authors thank the German Weather Service (DWD) for providing meteorological data set. The staff of the IGB chemical analytics and biogeochemistry lab were appreciated for NO‐N analyses, and for compiling the long‐term DMC data set. The authors also thank Aaron Smith for supports in terms of comparing results between mHM‐Nitrate and EcHO‐iso.Peer reviewedPublisher PD

Hesitant Triangular Fuzzy Information Aggregation Operators Based on Bonferroni Means and Their Application to Multiple Attribute Decision Making

We investigate the multiple attribute decision-making (MADM) problems with hesitant triangular fuzzy information. Firstly, definition and some operational laws of hesitant triangular fuzzy elements are introduced. Then, we develop some hesitant triangular fuzzy aggregation operators based on Bonferroni means and discuss their basic properties. Some existing operators can be viewed as their special cases. Next, we apply the proposed operators to deal with multiple attribute decision-making problems under hesitant triangular fuzzy environment. Finally, an illustrative example is given to show the developed method and demonstrate its practicality and effectiveness

Identifying Dominant Processes in Time and Space: Time‐Varying Spatial Sensitivity Analysis for a Grid‐Based Nitrate Model

Distributed models have been increasingly applied at finer spatiotemporal resolution. However, most diagnostic analyses aggregate performance measures in space or time, which might bias subsequent inferences. Accordingly, this study explores an approach for quantifying the parameter sensitivity in a spatiotemporally explicit way. We applied the Morris method to screen key parameters within four different sampling spaces in a grid‐based model (mHM‐Nitrate) for NO3‐N simulation in a mixed landuse catchment using a 1‐year moving window for each grid. The results showed that an overly wide range of aquatic denitrification rates could mask the sensitivity of the other parameters, leading to their spatial patterns only related to the proximity to outlet. With adjusted parameter space, spatial sensitivity patterns were determined by NO3‐N inputs and hydrological transport capacity, while temporal dynamics were regulated by annual wetness conditions. The relative proportion of parameter sensitivity further indicated the shifts in dominant hydrological/NO3‐N processes between wet and dry years. By identifying not only which parameter(s) is(are) influential, but where and when such influences occur, spatial sensitivity analysis can help evaluate current model parameterization. Given the marked sensitivity in agricultural areas, we suggest that the current NO3‐N parameterization scheme (land use‐dependent) could be further disentangled in these regions (e.g., into croplands with different rotation strategies) but aggregated in non‐agricultural areas; while hydrological parameterization could be resolved into a finer level (from spatially constant to land use‐dependent especially in nutrient‐rich regions). The spatiotemporal sensitivity pattern also highlights NO3‐N transport within soil layers as a focus for future model development.Chinese Scholarship CouncilLeverhulme Trust http://dx.doi.org/10.13039/501100000275Einstein Stiftung Berlin http://dx.doi.org/10.13039/501100006188Berlin University Alliance http://dx.doi.org/10.13039/501100021727Peer Reviewe

Identifying Dominant Processes in Time and Space : Time-Varying Spatial Sensitivity Analysis for a Grid-Based Nitrate Model

Funding Information: Songjun Wu is funded by the Chinese Scholarship Council (CSC). Contributions from Chris Soulsby are supported by the Leverhulme Trust through the ISO‐LAND project (Grant Nos. RPG 2018 375). Tetzlaff's contribution was partly funded through the Einstein Research Unit “Climate and Water under Change” from the Einstein Foundation Berlin and Berlin University Alliance. We thank the German Weather Service (DWD) for providing meteorological data set. The staff of the IGB chemical analytics and biogeochemistry lab are thanked for compiling the long‐term water quality data set in DMC. Open Access funding enabled and organized by Projekt DEAL. Chinese Scholarship Council Leverhulme Trust. Grant Number: RPG 2018 375 Einstein Stiftung Berlin Berlin University Alliance Publisher Copyright: © 2022. The Authors.Peer reviewedPublisher PD

A Multi-Objective Routing Algorithm Based on Auction Game for Space Information Network

This paper aims to create a resource-saving method for the routing problem in space information network. To this end, a multi-objective routing algorithm was created based on game theory for space information network. Specifically, the auction game was introduced to solve the routing problem using the delay-tolerating network (DTN) protocol. Considering the topological periodicity of low earth orbit (LEO) satellite network, a typical space information network, the dynamic topological structure was divided into relatively static time slots. Then, the routing problem was solved through the auction game in these slots. The proposed algorithm can minimize the number of selfish nodes in the network and avoid network congestion resulted from excessive resource consumption of individual nodes. Finally, the proposed algorithm was compared with other well-known routing models like the epidemic routing model (Epidemic) and the first contact routing model (FC). The results show that the proposed algorithm outperformed the contrastive models in both average delay and network overhead ratio. The research findings shed important new light on the routing of space information network