A Coding-Based Steganography Using Multiple Frequency Domains

Abstract:In this paper, a new technique for hiding text in a bitmap images will be present. The technique based on using an index of the dictionary representing the characters of the secret messages instead of the characters themselves. The technique uses multiple frequency domains for embedding these indexes in an arbitrary chosen bitmap image. By using discrete cosine transform DCT, discrete wavelet transform DWT, and a combination of both of them. ِA software package for implementing this technique are built and we got very good results in terms of capacity of hiding, imperceptibility which are the most two important properties of steganography, the time of hiding the text, and the security issues

A study on the false positive rate of Stegdetect

In this paper we analyse Stegdetect, one of the well-known image steganalysis tools, to study its false positive rate. In doing so, we process more than 40,000 images randomly downloaded from the Internet using Google images, together with 25,000 images from the ASIRRA (Animal Species Image Recognition for Restricting Access) public corpus. The aim of this study is to help digital forensic analysts, aiming to study a large number of image files during an investigation, to better understand the capabilities and the limitations of steganalysis tools like Stegdetect. The results obtained show that the rate of false positives generated by Stegdetect depends highly on the chosen sensitivity value, and it is generally quite high. This should support the forensic expert to have better interpretation in their results, and taking the false positive rates into consideration. Additionally, we have provided a detailed statistical analysis for the obtained results to study the difference in detection between selected groups, close groups and different groups of images. This method can be applied to any steganalysis tool, which gives the analyst a better understanding of the detection results, especially when he has no prior information about the false positive rate of the tool