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By Newman, Moskowitz, Chang, Brahmadesam
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Extra info for A Steganographic Embedding Undetectable by JPEG Compatibility Steganalysis
A priori class probabilities are cal(0) culated qi = ψi (Z0 ), i = 1, 2, . . , L and the class number i(0) , say, is drawn at random with these probabilities. 2) Values of parameters of pdf in class i(0) are established according to (0) θi = φi (Z0 ), i = 1, 2, . . , L and the vector of features X0 , say, is drawn at random from the distribution (0) fi(0) (x; θi ) = fi(0) (x; φi (Z0 )). (1) It is clear that the above scheme includes the classical pattern recognition problem statement as a special case (ψi ’s and φi ’s are constant functions).
The left panel of this ﬁgure shows the number of black pixels registered by the camera in a bright room on subsequent frames. Selecting the threshold tb = 2000 from the learning sequence (lower curve), one can easily read out from the upper testing curve that we commit about 3% of errors. If this threshold is applied to the observations, which are shown in the right panel, then all the observations would be classiﬁed to the class ”no smoke present”, leading to about 40% of errors. The reason is that these observations were made by the camera in an almost dark room.
Rafajlowicz Fig. 1. Left panel – number of black pixels vs frame number when smoke is (not) present in a bright room and in a dark room – right panel (learning sequence – boxes, testing sequence – circles) the gray level of each pixel with a threshold. The total number of pixels, which were marked as ”black” was used as only one feature indicating the possible presence of smoke. It is well known that for classiﬁcation to two classes, which is based on one feature only, it usually suﬃces to select a threshold separating classes.