i so my name is patrick bob um from france of a little uh uh and also france and so this talk is gonna be a uh what i'm knocking security a mostly out to design secure well uh and baiting scheme and then will show you how to use a nice mess medic zero record optimal to once per in order to minimize the distortion and and also to guarantee what um security um so the line is very simple a first introduction on the and what are mocking security is and uh i will uh propose requirement in order to achieve secure and baiting uh i would propose so different embedding scheme and compare them using a performance and that is this and then will conclude my work after the what okay uh what's mean security in what are marking so a security as thing to do is do you picked compression uh no what at to share additional like go and uh channel eight uh you have to consider a first and that this ad that's every it's is very important because the are just every as the brain is able to try to hack you'll system to perform at that it can i have a a a really do a an immediate problem in a uh a a where E it is gonna depend of your uh a now you that you're gonna can see there but uh this is some ones that would try so two hard your system that that's sorry you have to consider that yeah as a bunch of my tell you or than that that you can be for example the what tell the code they'll detect detector then you would be able to yeah form more what we code or equal attack um here we go that can see there's that's the at necessary will have i con turns that that what they mapped to read the and a a what a map with the same key you can have all of L scenario where you assume that the just every for example knows the ms that are embedded in so but a map content T pretty depends of the signal you and a strong assumption is that uh you assume that to what am is a um uh that that sorry know everything about the but they marking scheme except the secret key this this is also one the or so as assumption and what are than the is objective of this uh the adversary re in all cases than a be to estimate you secret key and uh if it's it's to make what they're are all copies of sage okay and that got that don't actually a lot about what is going to a a a a what the is that the stories is uh doing to do because it in all case we can that the uh uh design secure embedding scheme so uh here are the assumption that we are are using we can see they are that the almost is good shown uh is C is this makes sense to sends to the some limit though is your M of course um and there's all assumption is the fact that uh as a horse is i I D in the in there i and in plea uh distributed we assume that all the content uh what them up using the same secret key and is this means that there is a and that that board W A a that map content only at that so we have in this frame the um i just necessary uh as only what am i can turn to does not know about them but didn't nice H for example or was the or here out to design uh secure scheme uh and also sorts all to play with a to was also a very strong can trends and what that masking meanings the and weighting distortions that has to be minimised and also to maximise the robustness okay i to minimize the or uh let's and the robustness so yeah we use um the rationale already use since a long time in what they're marking it's to use in it coding and you set side information from the almost is come from the famous close that's paper but one dove T paper or uh uh a what that marking and uh the I Ds and an I D to generate a different coding rage and that we'll code for the same may say and is an baiting in very simple in this case since you have different and dating regions in a to embed them a said you go to the close decoding regions and you uh uh you're able to minimize the distortion okay so no i will a some up the stream main we got crime i'm and fall secure them baiting so the first one that i called it distribution splitting you are have uh you want to achieve a a very strong statistical property chord is a perfect secrecy uh in steak and the refugee of take was security in what they marking it's means that the distribution of the content uh is the original content so P of X and the distribution of the what um not content i it E E Y given the key a exactly the same if you are of these properties and so just a re can not do anything about a uh i can that that that you'll system then because it doesn't know is a coefficient are what all marked or even uh is the carrier may age okay uh here we assume that we are doing a binary and baiting in means that actually you can't really you're your uh distribution of what that map the content P of Y into two distribution the one uh for of the ms a a zero and the one for the said one and of course you have to divide a two in order to normalize the distribution uh in a bill to achieve this predicting we go now use a partitioning function uh J G and uh you will see i would show you an example but basically once you have G A a a uh G you can compute the distribution and fall zero according to this partitioning function then you can compute the D be shown the what are not good content and bidding one also according to this partitioning function you're is an example of the J function so it's this lou uh that a wise uh function and so you see that the and function your is divided so it in in two parts here i just are you the part for one dating zero but you have also as you a part one bit one so the blue K here is the distribution one you want but you want but you if you add the two distributions of the chrome complain military terry one for dating one you of the now have the exactly the send distributions and the distribution of the also okay so it is is the first requirement distributions splitting now we have to find a map being in of the two go from the distribution of well can which is gauche shown with a assumption to is the new distribution when you want for example to embed bet one okay we can find plenty of different mappings of course so C we can that holds this mapping being uh function T get beat L T and so the requirement as a already say so you have to be to to this cribbage so the requirement is that you want to find this because the mapping that will minimize embedding dating distortion would do we so so this is important because you have but you can invent of different mapping but is only one that's will minimize the average at two T distribution and here we use optimal once spot in order to do this so the optimal tons pulse you re give a uh implicit form we'd out of the being in the is the case of a scan a distribution so in in one D it's a given by the mean it relative distribution function cat be tied at all of the density of the what that mark content uh i that is applied on the cumulative distribution function of the host content get beat that F four S this is the that the mapping from your you can also derive a is the and bidding being distortion it's right then here and so you can once you know the distribution and you can compute the mapping for he distribution uh you you shows them uh we're is an example so still a my oldest below the be shown than the target distribution and one dating is you were you a a one example of such a mapping here so this mapping being will and able to minimize at this the the distortion on average now is that everything is done you you you you you you can choose different partitioning function of your distribution you know either to uh that form baiting this is what was called the a once upon that you are what our marking so you the distribution is very simple if you want to one bad you will all the cool quite if what are not a coefficient of an i on the left side of the potion distribution if you want to embed one it gonna be on the right side this is not in at because you have only one a coding region of dictionary region uh and it was a it comes from a use what uh no or you can play with is the distribution and design new distribution here is an example where a or coding and as the set an uh are and we also the same probability so the the a here is the same than this one and so long so i called E he not you are what that marking be close all the distance is the region at the same probability P in all the two and as its and probably T P yes uh you can also see me try a seem that tries this uh D the and then you are P bar that you are what they marking it exactly the same than the previous one but i perform a tree according to a a or or uh around zero okay yeah again i managed to a of the pleading distribution requirement a and and they able to to from and dating the last partitioning function i tried that is called the no that natural are what a marking where a or or then beating uh region as the same way that is code then does so it's very similar you know to this can of post test scheme all to you a i am but on you know you have this uh secure and bidding in this case okay so i will now uh compels of defer and to uh what a mapping method the a regarding uh a tell of eight okay i want to evaluate knows the last constrains the robustness so i will uh i'm but now assume uh additive white caution noise uh channel and here is the compare is and between the that than that you are what that marking and france pop net you are what that marking needs just to evaluate the benefit of doing inform it coding and as you can see so he's low well well a plot is for that than that you are what that marking so you can see that you can that shape uh small of bit error rate once you can see that in front of coding spatially for lower that uh the value you in a the you and a the what them map and of the ratio of cool so this is so of first benefit no if i can they so as a different uh and they so that than that you to what they marking P that you are what that mapping and P bound that you are what yeah marking again a again but if they are and what are marked to knows the ratio you can see is that for example in this case we have a what they map to content which sure of minus five db in this case so first the P uh and that you are without marking gives the best yeah formants below minus five db and that the are you have to use the P bound that you are what that marking in of the two uh degrees you're a bit error rate if i i degrees again to what that mark to content issue i i uh no sorry yeah um i wanted also to compare now we is and secure but known to be robust what the marketing scheme so you have to mail for to more plots is that got more less picky read for uh in bits picked one here so you can see for example that the improve a like all for of course low well bit error right for the value and and and this get us but that's scheme E which fill also low low well it or right so oh is this is a kind of a known in in the secure what they marking it's very out to be both robust and very secure this the rubber scheme yeah wow a more robust and the secure one and knows of embedding distortion and minus eleven D V yeah you can see is that main is the uh that than that you are what that marking out their forms the was a a a a uh and dating scheme so the bit the it much real well for this one if again i compare our we is this can ask what S T in uh and uh is the input speech spectrum i can see is that the the kind of "'cause" that's in give even beta a bit error rate even if as a at the the value and they to zero you are very you are very close performance as so it is also interesting no i will conclude group michael so um it's its importance so to perform distributions coefficients so to split your this or and you know up to achieve a secure and baiting you have also to find a way to match the two distributions so the one of the all sins one of the what they not go though in order to do so uh a to do this you can use of to much from sports it's very it's walks fine in one dimension mention is the problem is that in a a multi dimensional it's more complicated but you can use all also of optimization trees and mean know up to minimize the distortion uh there is a gain of using in from it in also for secure and baiting and uh and thought and that is so the best partitioning depends of the and dating used option the but use yeah and also of the noise so i we don't have a a a a a magic old up to perform both secure and and and dating the best pick either a fell to link what we can do using secure adaptation of the get us but that's you so they have been applied by around the last week and is using at at at that position of a uh this yeah that's but that's scheme and they are or so a more fundamental problems as like i would to compute exactly theoretically is a secure get by is so how much information they can convey a while keeping being uh security and also another problem is that if you want to what secure embedding betty a secure uh and baiting uh with is low bit error rate you have also to use secure uh iraq weighting yeah well only can code and this is uh not known right now also we have to find a way to improve the quality of cell is but also guarantee security it's not easy because if you use uh they're all calling encode you gonna add dependence between the symbols that you're gone and that and this dependence can be a security uh equal thank you for your attention and the time for a couple that to a kind of nice the didn't on the first like to set you word and work on a C is just a done the quite sure what type of security does your net the dress and and is you cure in the first place because you kind of i use you could in first two slice you never argue why he's Q also okay so uh here are the signal you is that so that very will have only what a market the content and we try to it's steam so you will have a bunch a what a market contents all more more to be uh what L mark only a set of then will be what they matt and we assume that all this can this as a cushion and and i a E okay so the goal of that the we will be two for example is to the set oh seven that uh convey so what am a so if you want to do this i i has to use some kind of uh uh security at that it can be for example looking at the distribution fine cluster yeah from independent can it uh and component and that is this would you have very very different way to perform from uh what uh what am looking at that in the security uh a lot at the security level here we uh i assume that is there is not possible at that since all the coefficient have the same distributions so what it is that the sre won't be a bore to know which coefficient i what alma and still not quite a what what what is the goal of the attacker i'm still not getting a so what is that the vet analysis that you trying to event of time so it's to if you at this you know you a few out of the N coefficient and only you set of and coefficient conveys or information but is the N coefficients the location of this and clean fish and uh come from us secret key so the of the idea so you will be two uh try to estimate which coefficient fusion conveys or information okay so and then then the next why that's the case why is this in system because it didn't see any secure you know i only solar boston's in yeah the security is grounded because you assume because uh with the em baiting you have exactly the same distribution before and after of baiting it's means that if i want if i plays a but yeah i and and the i am that that's every i won't be a ball to locate which coefficient carries a what that matt so if i want to to try if i tells a say J you have to add the noise on all the coefficient of my team i cannot say act which one carries a a age as a a just every a left so offline yeah right so thank you know match again