A Natural Steganography Embedding Scheme Dedicated to Color Sensors in the JPEG Domain

Abstract

Using Natural Steganography (NS), a cover raw image acquired at sensitivity ISO 1 is transformed into a stego image whose statistical distribution is similar to a cover image acquired at sensitivity ISO 2 > ISO 1. This paper proposes such an embedding scheme for color sensors in the JPEG domain, extending thus the prior art proposed for the pixel domain and the JPEG domain for monochrome sensors. We first show that color sensors generate strong intra-block and inter-block dependencies between DCT coefficients and that theses dependencies are due to the demosaicking step in the development process. Capturing theses dependencies using an empirical covariance matrix, we propose a pseudoembedding algorithm on greyscale JPEG images which uses up to four sub-lattices and 64 lattices to embed information while preserving the estimated correlations among DCT coefficients. We then compute an approximation of the average embedding rate w.r.t. the JPEG quality factor and evaluate the empirical security of the proposed scheme for linear and non-linear demosaicking schemes. Our experiments show that we can achieve high capacity (around 2 bit per nzAC) with a high empirical security (P E 30% using DCTR at QF 95).

Computing Dependencies between DCT Coefficients for Natural Steganography in JPEG Domain

“…(4) Sampling on four lattices. In [16], the authors have shown that for this development, the stego signal generated on two non 8-connected blocks is independent and that the dependencies between 8-connected blocks are solely due to demosaicking. Consequently, we can use four lattices Figure 2 to sample the stego signal in the DCT domain.…”

“…When these dependencies are not taken into account, the embedding scheme becomes highly detectable at high JPEG Quality Factors (QF). To overcome this problem, the authors of paper [16] modeled these dependencies using the multi-variate Gaussian model with the covariance matrix of the stego signal in the DCT domainΣ estimated from a constant-luminosity RAW image altered by shot-noise. The embedding was then designed to respect the required covariance among stego DCT coefficients.…”

“…< l a t e x i t s h a 1 _ b a s e 6 4 = " ( n u l l ) " > ( n u l l ) < / l a t e x i t > < l a t e x i t s h a 1 _ b a s e 6 4 = " ( n u l l ) " > ( n u l l ) < / l a t e x i t > < l a t e x i t s h a 1 _ b a s e 6 4 = " ( n u l l ) " > ( n u l l ) < / l a t e x i t > < l a t e x i t s h a 1 _ b a s e 6 4 = " ( n u l l ) " > ( n u l l ) < / l a t e x i t > Figure 1: Overview of the embedding scheme presented in [16].…”

“…In this paper, we propose another high capacity steganographic scheme for the JPEG domain that relies on the principles of modelbased and side-informed steganography. It also belongs to the class of Natural Steganography (NS) [1,2,4,16] that generates the stego signal by mimicking the sensor noise associated with a larger ISO sensitivity. Section 2 reviews the principle of NS and its recent implementation in the JPEG domain.…”

Hide Secret Information in Blocks: Minimum Distortion Embedding

“…The hiding rate or embedding rate of a image can be computed as [4], [26], Embeding rate = Number of secret bits Capacity of encoding × 100 Both MDE and F5 methods were tested with Support Vector Machine to detect steganalysis probability, the following comparison are prepared after getting the steganalysis detection result. During the performance testing, the error probability and embedding rate were considered with both QF = 50, and QF = 75.…”