Generation of Authentication Signals

In this section, the proposed technique for composition of authentication signals is described. In Section 4.2.1, the principle is described first, and in Section 4.2.2, the proposed process for generation of authentication signals is presented.

4.2.1 Principle of Authentication Signal Generation

In this study, a frame group is treated as a unit for authentication. Therefore, each frame group G of an input video has its own authentication signals which comprise two parts. The first part is the index of G which is used to detect temporal tampering. The second part is tree structured macroblock decomposition information T of the motion regions of a P frame within G, which is selected randomly by a key.

Since surveillance videos usually contain some suspicious activities in motion regions, we use the motion regions to generate the authentication signals. More specifically, we record T and quantify it to form a string I. Then, I together with the index of G comprise the authentication signals S_b. Finally, S_b is embedded into the I frame of G for the authentication use.

4.2.2 Process for Generation of Authentication Signals

In Chapter 2, we have reviewed the tree structured motion compensation technique of the H.264 standard. A 16×16 macroblock can be partitioned by one of the following ways: one 16×16 macroblock partition, two 16×8 partitions, two 8×16 partitions, and four 8×8 partitions. If the 8×8 macroblock partition mode is chosen,

each of the four 8×8 sub-macroblocks in the macroblock may be further partitioned in four ways: one 8×8 sub-macroblock partition, two 8×4 sub-macroblock partitions, two 4×8 sub-macroblock partitions, and four 4×4 sub-macroblock partitions.

Therefore, a macroblock partition mode and four sub-macroblock partition modes are used to describe the partition of a 16×16 macroblock in the H.264 standard.

Besides the index of a frame group G, we also need the tree structured macroblock decomposition information T of a P frame within G, which is selected randomly, to generate the authentication signals. Specifically, we select one of the P frames F_p in G and perform the motion detection algorithm introduced in Chapter 3 to Fp to obtain a set of the motion regions, R, in it. For each 16×16 macroblock M of each region R_i of R, denote its macroblock partition mode as P_m. If P_m is a large partition mode except for the 8×8 macroblock partition, we use the bit 0 to represent M. If P_m is the 8×8 macroblock partition and each sub-macroblock partition mode of M is the 8×4, 4×8 or 4×4 mode, we use the bit 1 to represent M. If Pm is the 8×8 macroblock partition mode and all the sub-macroblock partition modes of M are the 8×8 sub-macroblock partition modes, then we treat M as a special case which will be described later.

Next, we form a binary string S′ with a pre-defined length LT to represent the tree structured macroblock decomposition information of R_i by assigning each macroblock a representing bit in the above-mentioned way. We then combine S′ with the binary form G_b of the index of G and the binary form R_ib of the coordinates of R_i to compose a binary string Si with length LR which is the sum of the value of LT, the length of Gb

and the length of R_ib. Moreover, we call S_i the region signal of R_i. Finally, we combine all region signals of R to produce the authentication signals Sb of G. The following algorithm describes the details of the above-mentioned process.

Algorithm 4.1. Process for generating authentication signals.

Input: a frame group G in a video, a secret key K, and a random number generator f.

Output: authentication signals Sb to be embedded.

Steps:

1. Use the input secret key K as a seed for f and use f to generate a sequence of random numbers, Q.

2. Select randomly a P frame F_p in G according to Q.

3. Perform the motion detection process (Algorithm 3.1) to Fp to obtain a set of the motion regions R in F_p.

4. For each region Ri within R, perform the following steps.

4.1 For each 16×16 macroblock M in R_i, perform the following steps.

4.1.1 Denote the macroblock partition mode of M as Pm and the sub-macroblock partition mode as P_s.

4.1.2 If Pm is the 16×16, 16×8, or 8×16 mode, Mark M as a large partition macroblock.

4.1.3 If Pm is the 8×8 mode and each Ps of M is 8×4, 4×8, or 4×4 mode, Mark M as a small partition macroblock.

4.1.4 For the case that both Pm and Ps are the 8×8 mode, decide that M is a large partition macroblock or a small partition macroblock according to the following rules.

4.1.4.1 Evaluate the partition score of M according to the following rules.

4.1.4.1.1 Name the eight neighboring macroblocks as A through H, as depicted in Figure 4.4.

4.1.4.1.2 Define the macroblock gain G_i for each of A through H in the following way.

(1) For A, B, C, and D, if P_i is the 8×8 mode, set the value of Gi to 1; otherwise, to 0.

(2) For D, E, F, and H, if P_i is the 8×8 mode, set the value of Gi to 0.5; otherwise, to 0.

4.1.4.1.3 Calculate the partition score according to the following equation:

4.1.4.2 If the partition score is larger than a pre-defined threshold T, mark M as a large partition macroblock; otherwise, as small.

4.1.5 If M is a large partition macroblock, set B(M) to 1; otherwise, to 0.

4.2 For each Ri, select LT 16×16 macroblocks M1 through ML, each denoted as M_i, and combine all B(M_i) to form a binary string S′, where L_T is a

pre-defined length of signals. If the total number of macroblocks in Ri is smaller than L_T, allow repetition of using macroblocks in R_i.

4.3 Transform the coordinate information of Ri into the binary form and combine it with S′ to form a new binary string S_i. macroblock; otherwise, a small one. A partition score is calculated for M based on the

eight neighboring macroblocks to decide which case M belongs to. The macroblocks which are in direct contact with M (macroblocks A, B, C, and D in Figure 4.4) have much influence on M than macroblocks E through H.

The case that the macroblock partition mode is 8×8 and the sub-macroblock

4.3 Embedding and Extracting of