Chaos-based Color Image Steganography Method Using 3 D Cat Map

Steganography is a technique to hide a secret message within a different multimedia carrier so that the secret message cannot be identified. The goals of steganography techniques include improvements in imperceptibility, information hiding, capacity, security, and robustness. In spite of numerous secure methodologies that have been introduced, there are ongoing attempts to develop these techniques to make them more secure and robust. This paper introduces a color image steganographic method based on a secret map, namely 3-D cat. The proposed method aims to embed data using a secure structure of chaotic steganography, ensuring better security. Rather than using the complete image for data hiding, the selection of the image band and pixel coordination is adopted, using the 3D map that produces irregular outputs for embedding a secret message randomly in the least significant bit (LSB) of the cover image. This increases the complexity encountered by the attackers. The performance of the proposed method was evaluated and the results reveal that the proposed method provides a high level of security through defeating various attacks, such as statistical attacks, with no detectable distortion in the stego-image. Comparison results ensure that the proposed method surpasses other existing steganographic methods regarding the Mean Square Error (MSE) and Peak Signal-to-Noise Ratio(PSNR).

Color image steganalysis based on embedding change probabilities in differential channels

It is a potential threat to persons and companies to reveal private or company-sensitive data through the Internet of Things by the color image steganography. The existing rich model features for color image steganalysis fail to utilize the fact that the content-adaptive steganography changes the pixels in complex textured regions with higher possibility. Therefore, this article proposes a variant of spatial rich model feature based on the embedding change probabilities in differential channels. The proposed feature is extracted from the residuals in the differential channels to reduce the image content information and enhance the stego signals significantly. Then, the embedding change probability of each element in the differential channels is added to the corresponding co-occurrence matrix bin to emphasize the interference of the residuals in textured regions to the improved co-occurrence matrix feature. The experimental results show that the proposed feature can significantly improve the detection performances for the WOW and S-UNIWARD steganography, especially when the payload size is small. For example, when the payload size is 0.05 bpp, the detection errors can be reduced respectively by 5.20% and 4.90% for WOW and S-UNIWARD by concatenating the proposed feature to the color rich model feature CRMQ1.