-By hiding data in compressed video using a secret key, we can prevent and eliminate the security threat faced by computers and smart devices in this information age. In this paper, we hid data by focusing on the motion vectors used to structure and reframe both the frontward extrapolative and bi-directional in dense image frames. The selection of candidate set of these motion vectors are established on their affiliated prediction error. An adaptive threshold is sought for each frame (based on greedy approach) to gain lustiness while sustaining a modest error level. The secret data bit stream is inserted in the Least Significant Bit (LSB) of both parts of the candidate motion vectors. The approach is implemented and checked for hiding data in natural sequences of multiple blocks of frames and the outputs are measured. The measurement is based on minimum distortion to the reframed video and minimum overhead on the compressed video frame. The proposed approach is justified efficient in line the stated condition, and more valid when compare with a motion vector attribute-based approach.
INTRODUCTION
The term “Confidential data” typically denotes data
classified as restricted, according to a specific data
classification scheme needs to be properly secured via any
secured mechanism that will not reveal it presence to an
unauthorized party (steganography). This data hiding
mechanism in different stegano-graphic cover is a broad
field. It is a technique of embedding secret data in a media or
other source and still maintaining the integrity of the data. It
can be used to embed confidential information for
annotation, access control, content transaction tracking, copy
right protection and tampering detection [
This research focuses on internal changes of video compression process, precisely the motion estimation level. Choosing this level was based on the fact that the compressed video frames contents are internally processed during the encoding and decoding of the video which possess detection constraint when analyzed by applying image steganalysis techniques besides it is coded loosely, thus it is not susceptible to quantization distortions. Change of motion vector based on Magnitude and Phase angle attribute has been the fundamental bases of most research applied on secure data hiding in a stegano-graphic cover. Confidential message in (data) bits are securely hidden in Motion Vector with high frequency above some specified
One data bit of the message is securely placed at the Least Significant Bit (LSB) of each larger Candidate Motion Vectors component, which is encoded as a unique area in a specified location generated by where the motion estimation generated motion vectors. Using the variable macro block 8 by 8, 8 by 16,16by 8 and16 by 16sizes of H.264, using each 2 bits from the data bit stream to choose one size out of the four different sizes for the Motion Estimation process. Carefully considering the angular (phase angle) difference between two nearest CMV, we embed the massage inside the sample video. These CMV were chosen according to the respective motion vectors magnitude. Using the Phase Angle Difference in sectors between CMV, the message bit stream is encoded accordingly with initial constrain of the block matching to search for a magnitude within the selected region to be greater than the initial threshold as predefined. This approach is focused on determining the direct reversible technique to find, at the decoder the CMV which relied on the motion vectors attributes.
Achieving least level distortion to prediction error and data size overhead was approached differently in this paper which is based on the prediction error associated with each block. The major constraint is difficulty of figuring out the non-linear quantization process.
II.
A mechanism of data hiding in compressed domain that makes use of encoding and compression sequences was proposed in [2], this is usually applied during different compression phase of after the compression phase of the video sequences. An optimal output with less image distortions can
me efficiently achieved when spatial and temporal model usually known as spatiotemporal model are concurrently used.
In [
In [
After the and quantization phases, follows the embedding process for some 4 by 4 block selected from each of 16 by 16 macro-blocks for embedding macro-block where the blocks with the larges Least Non Zero ( ) level arrangement are selected which simply define the frequency sample. For every selected block, 1 bit of message is embedded. The security of this mechanism
was improved by using the secretkey for authentication which is encrypted by a pseudo randomly generated key to designate the watermark point. = ( : ) (1)
Another approach of securely hiding information was
proposed in [
This technique was mathematically modeled as precisely stated below, given that, watermark is represented as coefficient is of image Z, for every high security measure, , encryption of watermark with a given a stream cipher before and initial the process, we have the following:
Change in value of the coefficient is as: ′ =
, ┌( | |)┐ % 2 = 1 . . , , | | % 2 = 0 Where = ┌
┐ + 1
′ = . . , ,
% 2 = 1 ≠ − (3) , encrypted key is placed at the image corners and then the hidden image is convey via a stego-cover. The recipient of the message has a foreknowledge of all the encrypted keys hidden at the corner of the image. A reverse technique called Reverse Data Hiding (RDH) mechanism is applied to the encrypted image which contains the original message; this is only possible when all the encrypted corners are properly decrypted to using the appropriate secret key.
A new concept of data hiding using visual cryptography
was presented in [
The receiver system performs video decryption accordingly, via a decryption splitting mechanism in which the system decrypts and split received video into separate units of frames and extracting various shares and data by selecting the frames which was watermarked
Another novel (and secure) data hiding mechanism was
suggested by [
vector for carrying out embedding process is calculated as follows: = Where; = the “vertical component of motion vector” = the “horizontal component of motion vector”
The mathematical steps for sequential data embedding are stated thus:
the range of;
For data bit = 0, search for 2 and 2 +1 within 0 < 2 − 2 +1 ≤ 180
For data bit = 1, t search for 2 and 2 +1 within the range of; 180 < 2 − 2 +1 ≤ 360 (6) For all condition = Null, calculate a pair of motion vector that satisfied condition (i) and (ii) above.
In [
III.
OVERVIEW OF PROPOSED SYSTEM
As shown in the figure below, the proposed confidential data hiding approach in compressed video using secret key has it input value as video. This input value is subjected to separation process in which the motion images (video) are split into image frames. The subjection process follows immediately, which subjects the image frames to coding process to produce a compressed frame (compressed video), where the coding procedure is DCT and Huffman coding. The next system stage is the application of secret key to the frames. With key generation principle based on RSA algorithm, a random secret key is generated, applied to the secret data which is then securely inserted to the video cover in the LSB of the frame through the implementation of LSB algorithm. This automatically produces the new video referred to as Stego-video. At the receiving end, the secret data can be extracted by reversing the LBS procedure and secret key process; this is also called inverse LSB and secret key.
Confidential data is embedding process is don on two
separate pixels which are pixel andpixel as a cover for the
confidential data respectively [
And for, of pixel ≠ message 1, go to step 3.
Step 2: For ( , ) = message 2, all pixels remain unchanged. If the value of ( , )≠message 2, pixel = +1 or -1 Step 3: For ( − 1, )=message 2,pixel = -1. And
For ( − 1, )≠message 2; pixel = +1 We mathematically state the function ( , )as; ′ , ′ = ′ + ′ 2 (7)
From the above algorithm, since the new LSB method for matching procedure is +1 or -1 based on pixel position, mostly the adjacent pixels and the separate pixel within the closet position between cover image tends to be very insignificant. We can infer that while hiding data high quality is maintained.
This algorithm take input value as video and produce stegovideo as output. It also implements other simple encoding procedures: Step 1: Receive input value as video Step 2: Perform separation process (video into frames)
Perform secret key and data encryption.
This algorithm take input value as Stego-video and produce implements other simple procedures. Step 1: Randomly generate some large primes say and , nearer to a given key
Select a key within the range of and , compute
= ∗ , Step 4: compute ( − 1)( − 1)
Assume = 1; = 1 While ( , = 0) = + 1;
Take = 1 + ∗ While , = 0 ; = + 1 = 1 + ∗ = /
Our greedy search algorithm takes the following steps Step 1: For 9 + 8 = 17 as overall points, check the points Step 2: When the central 8closest points on the 3 * 3 grid concentrated at the middle nine points including the product grid and the eight closest points to 9 * 9 grids. If the search window center =least block distortion point (Integer DCT), terminate search; if not, proceed to step 2. is found to be the lowest in previous step, proceed to step 3; if not forward to step immediate step after step 3.
Step 3: Search until for winning point in step 1 above = 1 for window center in 3*3 searches.
minimum of 3 based on position of winning point =1. Then halt search.
Step 4: Determine half of 9 * 9 search window sizes choose center to be equal to minimum block distortion measure point in step 1 and repeat search procedure as step 2 and step 3 in Three Search Step
A video stream of 89Mb was separated into three different frames, compressed, converted into binary codes using MATLAB) and subjected to message insertion (via LSB algorithm). The frames were reconstructed and an evaluation test of comparative performance of the reconstructed image frame (video) using size 256 X 256 for all frames from sample video was tested.
With the transformation process on specific block size of 8 by 8, an optimum result of the DCT coefficient was obtained. This was found to be 64 coefficients. With the variation range of 1 and 64, the various
and of the reconstructed image frames were compared with a defined standard image frame for different range of 1-64 as earlier used (i.e. excluding zero). The result obtained shows that data hidden in LSB of compressed video has little or no distortion to the original video. Based on the average PSNR estimation, a high PSNR value was obtained which proves high image quality of the reconstructed video which is similar to (if not almost the same as) the original video. At the receiving end, the whole insertion process was reversed. The experiment shows that concealed confidential data will be un-noticed even if the data is conveyed through an un-secured medium
As a criterion for good image quality, the parameter MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) are express below;
MSE(Mean Square Error) is an acronym for the
meansquared error. It measures the error difference between a
given
stego-image
and
it
covers [
,
=1 =1 − 2 (9) = =
Value of a given image in pixel at location , in the cover image Value of a given image in pixel the same location in the fitting stego-image. (Peak to peak value of the original data )2 (8) Where E= log10
The standard unit of measuring PSNR is usually designated as value for quality consideration. This means high value of PSNR signifies good quality of image
ANALYSIS OF PSNR VALUES FOR DIFFERENT TEST
SEQUENCES
From the PSNR sequences graph, the sequence of Ship1 and Ship 3 has high motion dynamics while Ship 2 has moderate upper finger motion. We evaluated this algorithm and compared PSNR value of original information image versus extracted image for different input videos
V.
CONCLUSION AND FUTURE WORK
We were able to come up with an unfailing, optimally secure technique for hiding confidential information in a stego-medium. The above work still further it experiment via a three step greedy search process to choose a suitable value for threshold to be used in selecting macro-blocks that match to a given CMV in order for identically identified of candidates the decoder in-spite of the condition that a macroblocks is not strongly compressed. The extraction of embedded image was found to be easy with almost no distortion to original message (massage integrity was absolutely maintained). This approach was compared with other massage hiding techniques gathered from different literatures and it was found more efficient, robust and reliable. Future work will focused on further increase in size of the “embedded payload” while maintaining efficiency, robustness and message integrity.