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.

RAS ID

52211

Document Type

Journal Article

Date of Publication

1-1-2022

Volume

25

Issue

1

Funding Information

Edith Cowan University

School

School of Engineering

Creative Commons License

Creative Commons Attribution-Share Alike 4.0 License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.

Publisher

Institute of Advanced Engineering and Science

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

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Link to publisher version (DOI)

10.11591/ijeecs.v25.i1.pp329-338