IoTSteg: ensuring privacy and authenticity in internet of things networks using weighted pixels classification based image steganography

Internet of Things (IoT) networks are considered the great challenging by emerging technologies that try to solve the problems in modern life, while securing the information still vital in different systems in research. A simple, but yet efficient, steganography based on the pixels characteristics of the cover image in the spatial domain is proposed in this paper. The cover image pixels are classified into highly smooth (HS) and Less Smooth (LS) domains to select the extra eligible pixels that occur in the HS region. The proposed classification scheme, named IoTSteg, achieves an embedment process different from other traditional steganography that use the total number of pixels. The performance of the developed new steganography is evaluated using the measures such as the Peak Signal to Noise Ratio (PSNR), Capacity-Distortion Trade-Off (CDTO) and Structural Similarity Index (SSIM). The results are compared with the schemes including the Statistical Features Maintained (SFM_A and SFM_B) and Difference Histogram Shifting (DHS). The secret test using 8000 bits and 4 cover images each size $$512\times 512$$ produced a PSNR value of 66.61, CDTO, and SSIM of 0.9998. The competence of the proposed scheme is determined under two embedding rates of 0.1 and 0.2 bit per pixel maintaining the high level of imperceptibility of the cover images. Detail analyses showed that the proposed steganography achieved excellent competence for the embedment procedure with two rates (HS and LS) to maintain the high level of the cover images imperceptibility.