A Robust and Reversible Watermarking Algorithm for a Relational Database Based on Continuous Columns in Histogram

Due to the discreteness of integer data, there are a large number of gaps and continuous columns in the histogram based on integer data. Aiming at the characteristics, this paper presents a robust and reversible watermarking algorithm for a relational database based on continuous columns in histogram. Firstly, it groups the database tuples according to the watermark length and the grouping key. Secondly, it calculates the prediction errors and uses the absolute values of the prediction errors to construct the histogram. Thirdly, it traverses the histogram to find all the continuous columns and in turn, computes the sum of the height of each continuous column and selects the group of continuous columns that has the largest sum as the positions to embed the watermarks. FCTD (Forest cover type data set) is utilized for experimental verification. A large amount of experimental data shows that the method is effective and robust. Not only does the data distortion caused by shifting histogram columns not exist, but the robustness of the watermark is also greatly improved.

A reversible database watermarking method non-redundancy shifting-based histogram gaps

In relational databases, embedding watermarks in integer data using traditional histogram shifting method has the problem of large data distortion. To solve this problem, a reversible database watermarking method without redundant shifting distortion is proposed, taking advantage of a large number of gaps in the integer histogram. This method embeds the watermark bit by bit on the basis of grouping. First, an integer data histogram is constructed with the absolute value of the prediction error of the data as a variable. Second, the positional relationship between each column and the gap in the histogram is analyzed to find out all the columns adjacent to the gap. Third, the highest column is selected as the embedded point. Finally, a watermark bit is embedded on the group by the histogram non-redundant shifting method. Experimental results show that compared with existing reversible database watermarking methods, such as genetic algorithm and histogram shift watermarking and histogram gap–based watermarking, the proposed method has no data distortion caused by the shifting redundant histogram columns after embedding watermarks on forest cover type data set and effectively reduces the data distortion rate after embedding watermarks.

Long-Term Impacts of Fuel Treatment Placement with Respect to Forest Cover Type on Potential Fire Behavior across a Mountainous Landscape

Research Highlights: The impact of variation in fuels and fuel dynamics among forest cover types on the outcome of fuel treatments is poorly understood. This study investigated the potential effects of treatment placement with respect to cover type on the development of potential fire behavior over time for 48 km2 of forest in Colorado, USA. Our findings can inform the placement of fuel treatments in similar forests to maximize their effectiveness and longevity. Background and Objectives: Efficient placement of fuel treatments is essential to maximize the impact of limited resources for fuels management. We investigated how the placement of treatments with respect to forest cover type affected the rate of spread, size, and prevalence of different fire types for simulated wildfires for 50 years after treatment. Materials and Methods: We generated an analysis landscape consisting of two cover types: stands on southerly aspects had low rates of tree growth and regeneration compared to stands on northerly aspects. We then simulated 1) thinning treatments across 20% of the landscape, with treatments exclusively located on either southerly (‘south treatment’) or northerly (‘north treatment’) aspects; 2) subsequent tree growth and regeneration; and 3) wildfires at 10-year intervals. Finally, we used metrics of fuel hazard and potential fire behavior to understand the interplay between stand-level fuel dynamics and related impacts to potential fire behavior across the broader landscape. Results: Although post-treatment metrics of stand-level fuel hazard were similar among treatment scenarios, only the south treatment reduced rates of fire spread and fire size relative to no treatment. Most differences in modeled fire behavior between treatment scenarios disappeared after two decades, despite persistently greater rates of stand-level fuel hazard development post-treatment for the north treatment. For all scenarios, the overall trajectory was of shrinking fires and less crown fire behavior over time, owing to crown recession in untreated stands. Conclusions: Systematic differences among cover types, such as those in our study area, have the potential to influence fuel treatment outcomes. However, complex interactions between treatment effects, topography, and vegetation structure and dynamics warrant additional study.