2 This work considers digital images as covers and fondation is
3 spatial least significant-bit (LSB) replacement.
4 I this data hiding scheme a subset of all the LSB of the cover image is modified
5 with a secret bit stream depending on to a key, the cover, and the message to embed.
6 This well studied steganographic approach never decreases (resp. increases)
7 pixel with even value (resp. odd value) and may break structural symetry.
8 These structural modification can be detected by statistical approaches
9 and thus by steganalysis methods~\cite{DBLP:journals/tsp/DumitrescuWW03,DBLP:conf/mmsec/FridrichGD01,Dumitrescu:2005:LSB:1073170.1073176}
11 This drawback is avoided in LSB matching (LSBM) where
12 the $+1$ or $-1$ is randomly added to the cover pixel LSB value
13 only if this one does not match the secret bit.
14 Since probabilities of increasing or decreasing pixel value are the same, statistical approaches
15 cannot be applied there to discover stego-images in LSBM.
16 The most accurate detectors for this matching are universal steganalysers such as~\cite{LHS08,DBLP:conf/ih/2005,FK12}
17 which classify images thanks to extracted features from neighboring elements of noise residual.
19 LSB matching revisited (LSBMR)~\cite{Mielikainen06} have been recently introduced.
20 For a given pair of pixels, in which the LSB
21 of the first pixel carries one bit of secret message, and the relationship
22 (odd–even combination) of the two pixel values carries
23 another bit of secret message.
27 In such a way, the modification
28 rate of pixels can decrease from 0.5 to 0.375 bits/pixel
29 (bpp) in the case of a maximum embedding rate, meaning fewer
30 changes to the cover image at the same payload compared to
31 LSB replacement and LSBM. It is also shown that such a new
32 scheme can avoid the LSB replacement style asymmetry, and
33 thus it should make the detection slightly more difficult than the
34 LSBM approach based on our experiments
