Publication: Enhanced Flipping Technique to Reduce Variability in Image Steganography
Date
2021
Authors
Kamil S.
Abdullah S.N.H.S.
Hasan M.K.
Bohani F.A.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Steganography algorithms hide the secret message in the cover image and provide imperceptibility to the attacker. The Least Significant Bit (LSB) algorithm is the preferred data hiding method. In this method, the least significant bit of the cover image pixel is substituted with the secret data bit. However, this method provides high variability if k-LSB bits of the cover image is replaced with secret data bit. To reduce the variability, various optimization algorithms are deployed in image steganography. These algorithms search the optimal pixels in the cover image, and data hiding is performed using the k-bits LSB method. Several iterations of optimization algorithms have increased the time complexity upon achieving this goal. Therefore, in this paper, a data hiding approach is designed based on the flipping approach that reduces variability and provides lesser time complexity. In the proposed method, initially, data hiding is performed using the k-bit LSB method in the cover image, and stego image is obtained. After that, the absolute difference between the cover and stego image is determined and compared with the threshold value. If the absolute difference is higher than the threshold value, then the adjacent bit of the k-bit LSB method is flipped. This process reduces the variability because flipping the adjacent bit will make the pixel value of the stego image closer to the cover image. The simulation evaluates using various performance metrics upon testing on standard dataset images. The simulation results show that the proposed method provides lesser variability, good visual quality, lesser time complexity than Genetic and Bayesian Optimization algorithms and the existing flip method. � 2013 IEEE.
Description
Benchmarking; Discrete cosine transforms; Genetic algorithms; Image coding; Image enhancement; Mean square error; Signal to noise ratio; Bayes method; Cover-image; Data hidden; Embedding capacity; Flipping methods; Least significant bits; Medium; Optimisations; Variability; Visual qualities; Steganography