Pub. online:6 May 2020Type:Research ArticleOpen Access
Journal:Informatica
Volume 31, Issue 3 (2020), pp. 481–497
Abstract
Data hiding technique is an important multimedia security technique and has been applied to many domains, for example, relational databases. The existing data hiding techniques for relational databases cannot restore raw data after hiding. The purpose of this paper is to propose the first reversible hiding technique for the relational database. In hiding phase, it hides confidential messages into a relational database by the LSB (Least-Significant-Bit) matching method for relational databases. In extraction and restoration phases, it gets the confidential messages through the LSB and LSB matching method for relational databases. Finally, the averaging method is used to restore the raw data. According to the experiments, our proposed technique meets data hiding requirements. It not only enables to recover the raw data, but also maintains a high hiding capacity. The complexity of our algorithms shows their efficiencies.
Journal:Informatica
Volume 25, Issue 4 (2014), pp. 523–540
Abstract
Abstract
Reversible data hiding is a method that can guarantee that the cover image can be reconstructed correctly after the secret message has been extracted. Recently, some reversible data hiding schemes have concentrated on the VQ compression domain. In this paper, we present a new reversible data hiding scheme based on VQ and SMVQ techniques to enhance embedding capacity and compression rate. Experimental results show that our proposed scheme achieves higher embedding capacity and smaller average compression rate than some previous methods. Moreover, our proposed scheme maintains the high level of visual quality of the reconstructed image.
Journal:Informatica
Volume 19, Issue 4 (2008), pp. 477–486
Abstract
In this paper we propose and analyze a multilayer perceptron-like model with matrix inputs. We applied the proposed model to the financial time series prediction problem, compared it with the standard multilayer perceptron model, and got fairly good results.
Journal:Informatica
Volume 18, Issue 4 (2007), pp. 615–628
Abstract
This paper proposes a reversible data hiding method for error diffused halftone images. It employs statistics feature of pixel block patterns to embed data, and utilizes the HVS characteristics to reduce the introduced visual distortion. The watermarked halftone image can be perfectly recovered if it is intact, only a secret key is required. The method is suitable for the applications where the content accuracy of the original halftone image must be guaranteed, and it is easily extended to the field of halftone image authentication.
Journal:Informatica
Volume 10, Issue 2 (1999), pp. 231–244
Abstract
In this paper two popular time series prediction methods – the Auto Regression Moving Average (ARMA) and the multilayer perceptron (MLP) – are compared while forecasting seven real world economical time series. It is shown that the prediction accuracy of both methods is poor in ill-structured problems. In the well-structured cases, when prediction accuracy is high, the MLP predicts better providing lower mean prediction error.