Reversible Data Hiding Using Prediction-based Adaptive Embedding

[[abstract]]In this paper, we propose a new algorithm in reversible data hiding with prediction-based scheme. Reversible data hiding can be implemented with two types, one is by modifying the histogram of images, named the histogram-based scheme, and the other is by changing the difference value between neighboring pixels, called the difference-expansion-based (DE-based) method. Considering the ease of implementation, we employ the histogram-based scheme as the base, integrated with the DE-based methods, which is famous for the abundance in embedding capacity, in our algorithm. For hiding the secret information, the differences between original and predicted images are produced firstly, and they are intentionally altered to make reversible data hiding possible. By utilizing the advantages from the two types of methods, by change of histograms of difference values, global and local characteristics of original images can be utilized for hiding more capacity with acceptable quality of output image. With our method, it performs better in embedding capacity, image quality, and side information than conventional algorithm in literature. It also has the potential for the integration to relating algorithms for practical applications.[[sponsorship]]Korea Information Processing Society (KIPS)[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20121220~20121222[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Hong Kon

Hierarchy-based reversible data hiding

[[abstract]]In this paper, we propose a new method for reversible data hiding by employing the hierarchical relationships of original images. There are many parameters for accessing the performances of reversible data hiding algorithms, including the output image quality, the hiding capacity, and the overhead for decoding. Considering the ease of implementation and the little overhead needed for decoding, we employ modification of difference values between pixels by using histogram-based scheme with extensions to pyramidal structure by utilizing inherent characteristics of original images. By doing so, global and local characteristics of original images can be utilized for hiding more capacity with acceptable quality of output image. With our method, better performances can be obtained with enhanced image quality, the more embedding capacity, and comparable amount of side information for decoding. More importantly, the reversibility of our method is guaranteed, meaning that original image and hidden message can both be perfectly recovered at the decoder. Simulation results demonstrate that proposed method in this paper outperforms those in conventional algorithms.[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]紙本[[booktype]]電子版[[countrycodes]]GB

A refactoring method for cache-efficient swarm intelligence algorithms

[[abstract]]With advances in hardware technology, conventional approaches to software development are not effective for developing efficient algorithms for run-time environments. The problem comes from the overly simplified hardware abstraction model in the software development procedure. The mismatch between the hypothetical hardware model and real hardware design should be compensated for in designing an efficient algorithm. In this paper, we focus on two schemes: one is the memory hierarchy, and the other is the algorithm design. Both the cache properties and the cache-aware development are investigated. We then propose a few simple guidelines for revising a developed algorithm in order to increase the utilization of the cache. To verify the effectiveness of the guidelines proposed, optimization techniques, including particle swarm optimization (PSO) and the genetic algorithm (GA), are employed. Simulation results demonstrate that the guidelines are potentially helpful for revising various algorithms.[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]US