Watermarking with Side Information Information Technologies for IPR Protection

Outline Information-theoretic basis Channels with side information Writing on dirty paper Dirty paper codes

Basic Definitions H(X,Y) H(X) H(Y) H(X|Y) H(Y|X) I(X;Y)

Communication Rates Code rates: a property of a code Achievable rate: a property of a communication channel Due to the noise in the channel, even the best code will have some probability of errors. A rate is achievable if we can obtain arbitrarily low probability of errors by using codes with arbitrarily long symbol sequences and correspondingly large sets of messages. |M|: the number of distinct messages that can be encoded with sequences of length L R: the number of bits of information encoded in each symbol

Channel Capacity The capacity of a channel is defined as the maximum achievable rate for that channel, or more precisely, the supremum of all achievable rates. Capacity of arbitrary channels Any channel can be characterized by a conditional distribution Py|x(y), which gives the probability of receiving y when x is transmitted The capacity of a channel is

Capacity of AWGN Channels x y n x: a transmitted symbol, limited by a power constraint n: the additive noise, drawn from a Gaussian distribution with variance The capacity of such channel is

Viewing the Capacity of AWGN Channels Geometrically //p365 Figure A.1 Each code word of length L can be viewed as a point in an L-dimensional space. The power constraint restricts all code words to lie within a sphere, centered at zero. y lies within a sphere around x.

Writing on Dirty Paper Imagine that we have a piece of paper covered with independent dirt spots of normally distributed intensity, and we write a message on it with a limited amount of ink. The dirty paper with the message on it, is then sent to someone else, and acquire more normally distributed dirt. If the recipient cannot distinguish between the ink and the dirt, how much information can we reliably send?

Dirty Paper Channels m Transmitter x First noise source Receiver mn u y s n Second noise source The channel has two independent white Gaussian noise sources, generating s and n. Before the transmitter chooses a signal to send, x, it is told the exact noise s that will be added to the signal by the first source. The signal x is limited by a power constraint. The first noise source has no effect on the channel capacity!

A Dirty-paper Code for a Simple Channel //P137 Figure 5.14 The first noise is restricted to produce only one of two specific noise vectors: s=s1 or s=s2. Assume that s=s1, the transmitter must choose a vector x, limited by the power constraint p, to encode a message. Since s1 is known, it can choose a desired value of the sum u=x+s1 instead, and then sets x=u-s1. u must lie within a ball of (LP)1/2 centered at s1. The problem of choosing u is essentially the same as that of choosing a signal to transmit over a AWGN channel.

A Dirty Paper Code for A Simple Channel …. For the case the first noise source generates s2, the transmitter choose a u from within a radius (LP)1/2 ball centered at s2. If s1 and s2 are sufficiently different from each other, the transmitter can simply use two different sets of code vectors. The transmitter decides on a value of u based on the message m, and the vector will be added by s. It than transmit x=u-s.

A Dirty Paper Code for A Simple Channel The receiver receives y=x+s+n=u+n, and it will search for the code vector closest to y, and output the associated message. The resulting message is reliable.And the capacity is unaffected by the first noise.

Dirty Paper Codes for More Complex Channels (I) We would like to generate a set of code vectors such that, whatever the value of s, we will always find at least one code for each message within a distance of (LP)1/2 around it. Finding a set U of vectors and dividing them into subsets (cosets) Um corresponding to possible messages m. All codes in any given coset represent the same message, and they are widely distributed so that one of them will be probably close to s, whatever value s may take.

Dirty Paper Codes for More Complex Channels (I) Given a message m and a known noise vector s, the transmitter finds the member u of Um that is closest to s.It then transmit x=u-s. The receiver finds the member of U that is closest to the received vector y, and then identifies the received message by determining which coset contains the code vector.

Dirty Paper Codes for More Complex Channels (II) To achieve full capacity, exactly only one code word for each message within the ball around every given value of s is desired. We must ensure that there is always a code word for message G, that is, when one G code word is on the edge of the ball, another is always inside it.

Now we transmit x=a(u-s) where 0<a<1. After adding s, the result is u’=au+(1-a)s, which is a weighted average of u and s. The power constraint is now relaxed to (LP)1/2/a. Trade-off: the closer a is to 1, the less noise is added to u, but the more strict the power constraint during search is. The closer a is to 0, the greater the noise, but the more relax the power constraint is.

Similarity between Dirty Paper Channel and Watermarking System Original signal Noise source co n m Encoder Decoder m’ w cw c’w The first noise (dirty paper) s plays the role of the original signal. The transmitted signal x is the added pattern. The power constraint p expresses the fidelity constraint. The second noise n is the distortion occurred due to normal processing or malicious tampering. If a watermarking system behaves enough like the dirty-paper channel, its maximum data payload should not depend on the cover media.

Difference between Dirty Paper Channel and Watermarking System The original signal and subsequent noise are rarely Gaussian. The fidelity constraint represented as a power constraint, implying that the distortion is measured by the MSE distance measure, is a poor predictor of perceptual distance.

General Form of Channels with Side Information Transmitter x Py|x,s(y) y Receiver mn s For each use of the channel, t, a random value s[t] is drawn from a set of possible values, S. Before the transmitter choose a codeword to transmit, it is told what all values of s[t] will be during the entire transmission. The transmitter then choose a sequence of L values, x, and transmits them. The sequence received by the receiver, y[t], is a random value dependent on both x[t] and s[t]. The dependency is described by a conditional probability distribution, Py|x=x[t],s=s[t](y)

Capacity of Channels with Side Information s: the value of the side information known to the transmitter. u: the auxiliary variable. We can regard it as an element of a code word, u, chosen from a set U. It depends on the