Enhanced Flipping Technique to Reduce Variability in Image Steganography
Samar Kamil, Siti Norul Huda Sheikh Abdullah, Mohammad Kamrul Hasan, Farah Aqilah Bohani · IEEE Access · 2021
Steganography algorithms aim to hide secret messages in a cover medium to provide imperceptibility to an attacker. Despite becoming the preferred technique for data hiding, the K-bit least significant bit (LSB) provides high variability and negatively impacts the visual quality of the cover image. Thus, optimization algorithms have been proposed to reduce variability. However, optimization-based methods have some limitations, such as high complexity and computational time. Therefore, a flipping method was proposed to reduce variability and reduce computational time. However, it provides lower embedding capacity. The challenge is how to design a new flipping method that reduces variability, enhances visual quality, and increases the embedding capacity. Therefore, this work proposes a flipping method for data hiding to reduce variability and enhance visual quality and embedding capacity. First, we hide the secret data in the cover image using the k-bit LSB technique. Then, we calculated the absolute difference between the cover and stego images. If the absolute difference of the 4-LSB is higher than a threshold value, then the adjacent bit of the 5-bits LSB is flipped. This process reduces the variability because flipping the 5th bit will make the pixel value of the stego image closer to the cover image. The performance of the proposed method is validated using the mean square error (for variability), peak signal-to-noise ratio (for visual quality), and embedding capacity parameters and tested on some benchmark dataset images. The experimental results showed that the proposed method provides less variability, good visual quality, and high embedding capacity compared to genetic and Bayesian optimization algorithms, and the existing flip method.