2 This work considers digital images as covers and it is based on
3 spatial least significant-bit (LSB) replacement.
4 In this data hiding scheme a subset of all the LSBs of the cover image is modified
5 with a secret bit stream depending on 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 symmetry.
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.
20 LSB matching revisited (LSBMR)~\cite{Mielikainen06} have been recently introduced.
21 For a given pair of pixels, in which the LSB
22 of the first pixel carries one bit of secret message, and the relationship
23 (odd–even combination) of the two pixel values carries
24 another bit of secret message.
28 In such a way, the modification
29 rate of pixels can decrease from 0.5 to 0.375 bits/pixel
30 (bpp) in the case of a maximum embedding rate, meaning fewer
31 changes to the cover image at the same payload compared to
32 LSB replacement and LSBM. It is also shown that such a new
33 scheme can avoid the LSB replacement style asymmetry, and
34 thus it should make the detection slightly more difficult than the
35 LSBM approach. % based on our experiments
41 Instead of (efficiently) modifying LSBs, there is also a need to select pixels whose value
42 modification minimizes a distortion function.
43 This distortion may be computed thanks to feature vectors that are embedded for instance in steganalysers
45 Highly Undetectable steGO (HUGO) method~\cite{DBLP:conf/ih/PevnyFB10} is one of the most efficient instance of such a scheme.
46 It takes into account SPAM features (whose size is larger than $10^7$) to avoid overfitting a particular
47 steganalyser. Thus a distortion measure for each pixel is individually determined as the sum of differences between
48 features of SPAM computed from the cover and from the stego images.
49 Thanks to this feature set, HUGO allows to embed $7\times$ longer messages with the same level of
50 indetectability than LSB matching.
51 However, this improvement is time consuming, mainly due to the distortion function
55 There remains a large place between random selection of LSB and feature based modification of pixel values.
56 We argue that modifying edge pixels is an acceptable compromise.
57 Edges form the outline of an object: they are the boundary between overlapping objects or between an object
58 and the background. A small modification of pixel value in the stego image should not be harmful to the image quality:
59 in cover image, edge pixels already break its continuity and thus already contain large variation with neighboring
60 pixels. In other words, minor changes in regular area are more dramatic than larger modifications in edge ones.
61 Our proposal is thus to embed message bits into edge shapes while preserving other smooth regions.
63 Edge based steganographic schemes have been already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10}.
64 In the former, the authors show how to select sharper edge regions with respect
65 to embedding rate: the larger the number of bits to be embedded, the coarser
67 Then the data hiding algorithm is achieved by applying LSBMR on pixels of this region.
68 The authors show that this method is more efficient than all the LSB, LSBM, LSBMR approaches
69 thanks to extensive experiments.
70 However, it has been shown that the distinguish error with LSB embedding is fewer than
71 the one with some binary embedding~\cite{DBLP:journals/tifs/FillerJF11}.
72 We thus propose to take benefit of these optimized embedding, provided it is not too time consuming.
73 In the latter, an hybrid edge detector is presented followed by an ad hoc
75 The Edge detection is computed by combining fuzzy logic~\cite{Tyan1993}
76 and Canny~\cite{Canny:1986:CAE:11274.11275} approaches. The goal of this combination
77 is to enlarge the set of modified bits to increase the payload.
80 But, one can notice that all the previous referenced
81 schemes~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10}
83 with only considering the payload, not the type of image signal: the higher the payload is,
84 the better the approach is said to be.
85 Contrarily, we argue that some images should not be taken as a cover because of the nature of their signal.
86 Consider for instance a uniformly black image: a very tiny modification of its pixels can be easily detectable.
87 The approach we propose is thus to provide a self adaptive algorithm with a high payload, which depends on the
90 For some applications it might be interesting to be have a reversible procedure to compute the same edge detection pixel set for the cover and the stego image. For this, we propose to apply the edge detection algorithm not on all the bits of the image but only on all the bits without taking into consideration the LSB.
93 \JFC{Christophe : énoncer la problématique du besoin de crypto et de ``cryptographiquement sûr'', les algo déjà cassés....
94 l'efficacité d'un encodage/décodage ...}
95 To deal with security issues, message is encrypted...
97 In this paper, we thus propose to combine tried and
98 tested techniques of signal theory (the adaptive edge detection), coding (the binary embedding), and cryptography
99 (the encrypt the message) to compute an efficient steganography
100 scheme, which takes into consideration the cover image
101 an which is amenable to be executed on small devices.
103 The rest of the paper is organized as follows.
104 Section~\ref{sec:ourapproach} presents the details of our steganographic scheme.
105 Section~\ref{sec:experiments} shows experiments on image quality, steganalytic evaluation, complexity of our approach
106 and compare them to state of the art steganographic schemes.
107 Finally, concluding notes and future works are given in section~\ref{sec:concl}
