For a cover image consisting of many pixels having the same neighbor values, the hiding rate is high.
The characteristics for an arbitrary day of a month and local time were calculated using the linear interpolation between two neighbor values.
As for the light-colored pixels in the leftmost column and in the bottom row, only four neighbor values are considered so that they are separately treated, respectively.
To preserve the adjacency histogram as suggested in [11], the left and right neighbor values of every pixel in a selected subset should be the same.
As for the light-colored pixels in the leftmost column, only the right, up, down, and up-right neighbor values are considered, while the left, right, up, and up-right neighbor values are taken into account for the light-colored pixels in the bottom row.
Then, the "outliers" can easily be detected and suppressed by iteratively detecting the spatial weights that largely deviate with respect to their neighbor values, and substituting them by the local mean of their neighbors.
The result to be sent to the DAC can be simply taken as an output value by a lookup process, or, to be more precise, the value can be interpolated from the neighbor values to get the output.
So we perform the hiding on the subsets of pixels having the same five neighbor values (left, right, up, down, and up-right, denoted by 5-N in short) as shown in Figure 10, where the pixels marked in black are chosen as the neighbors of others, that is, only the light-colored pixels are grouped into a subset if they have the same five neighbor values.
The algorithm was implemented with on 1000 gray images provided by BOWS-2 [33] in the 5-N way, that is, on every subset of pixels having the same five neighbor values (left, right, up, down, and up-right).
Then we show that the steganalytic algorithms based on histogram characteristic function (HCF) can be prevented by implementing the algorithm on subsets of pixels having the same neighbor values.
