LSB Pseudorandom Algorithm for Image Steganography Using Skew Tent Map

In this paper, we propose a LSB steganography algorithm to hide pseudorandomly sensitive information in digital images. This algorithm affects the statistic features of the sensitive information before it is embedded in the cover image. Also, it satisfies the imperceptibility condition of the hidden information, required in steganography systems, by using a pseudorandom LSB technique endorsed on three chaotic noise generators based on the skew tent chaotic map. Additionally, the embedding capacity of the proposed algorithm can be up to 37% of the cover image size, satisfying the imperceptibility condition, when one bit of sensitive information is embedded per each embedding pixel. We evaluated the proposed algorithm using two criteria: cover image affectation and robustness against steganalysis attacks. To evaluate the cover image affectation, we considered metrics of texture, quality and perceptual quality. To evaluate the robustness against attacks, we use the StegExpose tool, which analyzes the images obtained from the proposed algorithm, and it includes the main methods of steganalysis such as Sample Pairs, RS Analysis, Chi-Square Attack and Primary Sets Analysis. Finally, we show that the proposed algorithm has similar performance, and even better, to three commonly used steganography tools.