X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/af81455342fce2b4d7f96ecb4f1194635a5a13a4..d5631915e956b49c89f78d66d7b4d25682a7e405:/intro.tex diff --git a/intro.tex b/intro.tex index 4c99548..a13492f 100644 --- a/intro.tex +++ b/intro.tex @@ -16,7 +16,7 @@ steganalysis methods~\cite{DBLP:journals/tsp/DumitrescuWW03,DBLP:conf/mmsec/Frid Let us recall too that this drawback can be fixed by considering the LSB matching (LSBM) subcategory, in which -the $+1$ or $-1$ is randomly added to the cover pixel's LSB value +a $+1$ or $-1$ is randomly added to the cover pixel's LSB value only if this one does not correspond to the secret bit. %TODO : modifier ceci By considering well-encrypted hidden messages, the probabilities of increasing or decreasing the value of pixels are equal. Then usual statistical approaches @@ -57,18 +57,16 @@ Thus a distortion measure for each pixel is individually determined as the sum of the differences between the features of the SPAM computed from the cover and from the stego images. The features embedded in WOW and UNIWARD are based on Wavelet-based -directional filter. Thus, similarelly, the distortion function is +directional filter. Thus, similarly, the distortion function is the sum of the differences between these wavelet coefficients computed from the cover and from the stego images. - - Due to this distortion measures, HUGO, WOW and UNIWARD allow to embed messages that are $7$ times longer than the former ones with the same level of indetectability as LSB matching. However, this improvement has a larger computation cost, mainly due to the distortion function -calculus. +computation. There remains a large place between random selection of LSB and feature based modification of pixel values. @@ -98,7 +96,7 @@ the one with some binary embedding~\cite{DBLP:journals/tifs/FillerJF11}. We thus propose to take advantage of this optimized embedding, provided they are not too time consuming. In the latter, an hybrid edge detector is presented followed by an ad hoc embedding. -The Edge detection is computed by combining fuzzy logic~\cite{Tyan1993} +The Edge detection is computed by combining fuz\-zy logic~\cite{Tyan1993} and Canny~\cite{Canny:1986:CAE:11274.11275} approaches. The goal of this combination is to enlarge the set of modified bits to increase the payload of the data hiding scheme. @@ -108,10 +106,27 @@ One can notice that all the previously referenced sche\-mes~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10} produce stego contents by only considering the payload, not the type of image signal: the higher the payload is, -the better the approach is said to be. -Contrarily, we argue that some images should not be taken as a cover because of the nature of their signals. -Consider for instance a uniformly black image: a very tiny modification of its pixels can be easily detectable. -The approach we propose is thus to provide a self adaptive algorithm with a high payload, which depends on the cover signal. +the better the approach is said to be. +For instance, studied payloads range from 0.04 to 0.4 modified bits per pixel. +Contrarily, we argue that some images should not be taken +as a cover because of the nature of their signals. +Consider for instance a uniformly black image: a very tiny modification of its +pixels can be easily detected. +Practically speaking, if Alice would send +a hidden message to Bob, she would never consider +such kind of image and a high embedding rate. +The approach we propose here is thus to provide a small complexity +self adaptive algorithm +with an acceptable payload, which +depends on the cover signal. +The payload is further said to + be acceptable if it allows to embed a sufficiently +long message in the cover signal. +Practically speaking, our approach is efficient enough for +payloads close to 0.06 bit per pixel which allows to embed +messages of length larger than 15,728 bits in an +image of size $512\times 512$ pixels. + % Message extraction is achieved by computing the same % edge detection pixels set for the cover and the stego image. % The edge detection algorithm is thus not applied on all the bits of the image, @@ -126,12 +141,12 @@ even in the worst case scenario, the attacker will not be able to obtain the original message content. Doing so makes our steganographic protocol, to a certain extend, an asymmetric one. -To sum up, in this research work, well-studied and experimented +To sum up, well-studied and experimented techniques of signal processing (adaptive edges detection), coding theory (syndrome-trellis codes), and cryptography -(Blum-Goldwasser encryption protocol) are combined -to compute an efficient steganographic -scheme, whose principal characteristic is to take into +(Blum-Goldwas\-ser encryption protocol) are combined in this research work. +The objective is to compute an efficient steganographic +sche\-me, whose principal characteristic is to take into consideration the cover image and to be compatible with small computation resources. The remainder of this document is organized as follows.