Document Type
Journal Article
Publication Title
Indonesian Journal of Electrical Engineering and Computer Science
Volume
25
Issue
1
First Page
329
Last Page
338
Publisher
Institute of Advanced Engineering and Science
School
School of Engineering
RAS ID
52211
Funders
Edith Cowan University
Abstract
Recent research has demonstrated the effectiveness of utilizing neural networks for detect tampering in images. However, because accessing a database is complex, which is needed in the classification process to detect tampering, reference-free steganalysis attracted attention. In recent work, an approach for least significant bit (LSB) steganalysis has been presented based on analyzing the derivatives of the histogram correlation. In this paper, we further examine this strategy for other steganographic methods. Detecting image tampering in the spatial domain, such as image steganography. It is found that the above approach could be applied successfully to other kinds of steganography with different orders of histogram-correlation derivatives. Also, the limits of the ratio stego-image to cover are considered, where very small ratios can escape this detection method unless modified.
DOI
10.11591/ijeecs.v25.i1.pp329-338
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.
Comments
Abdali, N. M., & Hussain, Z. M. (2022). Reference-free differential histogram-correlative detection of steganography: Performance analysis. Indonesian Journal of Electrical Engineering and Computer Science, 25(1), 329-338.
https://doi.org/10.11591/ijeecs.v25.i1.pp329-338