Open AccessReference-free differential histogram-correlative detection of steganography: performance analysis
Author(s) -
Natiq M. Abdali,
Zahir M. Hussain
Publication year - 2022
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v25.i1.pp329-338
Subject(s) - steganalysis , steganography , histogram , cover (algebra) , artificial intelligence , least significant bit , computer science , pattern recognition (psychology) , image (mathematics) , span (engineering) , mathematics , engineering , mechanical engineering , civil engineering , operating system
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.