a novel approach for increasing robustness and security of lsb-based digital audio watermarking

8/3/2019 A Novel Approach for Increasing Robustness and Security of LSB-Based Digital Audio Watermarking

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A Novel Approach for Increasing Robustnessand Security of LSB-Based Digital Audio

WatermarkingZahra Movahhedinia, Kamal Jamshidi

Abstract —The authenticity or ownership verification may be provisioned by digital watermarking which can be performed by

proper methods of steganography. In this paper we develop a robust Least Significant Bit (LSB) based audio watermarking

algorithm that uses a chained hash table (CHT) to attain the time complexity O(n), where n is the number of samples in the

cover data. To promote the robustness of our approach, the covert bits are embedded in the tenth LSB layer of the stego signal

using the regions where the energy of the audio signal is high. To lessen the computational complexity, an optimized jumping

window technique is employed. The robustness and imperceptibility of the proposed method is investigated and shown that

while simple and fast, it is more robust and secure than the standard LSB method.

Index Terms — Data Hiding, Audio Steganography, Digital Watermarking, LSB method.

—————————— ——————————

1 INTRODUCTION

IGITAL watermarking is the process of hiding in‐

formation into a digital media which may be used to verify its authenticity or identity of the owners.

Proper methods of steganography may be used for digital watermarking. Steganography is the art and science of transforming covert messages in a way that no one other than the sender and receiver suspects the existence of the message. In steganography the signal which the secret message will be inserted into is called the container or the cover data and once the message is hidden the generated signal is called the stego data. One of the earliest and

simplest methods

of

steganography

was

Least

Significant

Bit (LSB). In this procedure the right‐most bits of the cov‐er samples are replaced with the bits of the secret mes‐sage.

There are some critical attributes necessary to every watermarked media such as :

1. Imperceptibility : there should be no perceptible difference between the cover signal and the stego signal. In other words, the watermarking process should not degrade the quality of the media.

2. Robustness : different kinds of attacks could be ex‐erted on a stego signal intentionally or uninten‐

tionally to remove or destroy the watermark. These attacks contain additive noise, resampling, lossy compression, D/A and A/D conversion, filter‐ing, and geometrical attacks such as : cropping, scaling, and rotating. In a robust watermarking scheme these signal processes would not harm the watermark unless it degrades the quality of the stego media

3. Security : the secret message embedded in a wa‐termarked data should not be recognizable to an

unauthorized person. To gain this purpose, some‐times the secret message is encrypted and then embedded in the cover data.

There has always been a contradiction between ro‐ bustness and imperceptibility. Enhancing the robustness makes the watermark more perceptible and vice versa. SNR diagrams confirm this confliction.

There are some other watermarking attributes which

may be necessary in some situations :

4. Fastness : in some

applications,

specially

real

‐time

communications, the watermark process should be done quickly.

5. Capacity : some applications need to embed large amount of data in their media. Some watermark‐ing schemes concentrate on this characteristic.

LSB is a simple and fast watermarking scheme present‐ing a high embedding capacity. The main disadvantage of LSB is its poor robustness. Modifying the least significant bit of the cover samples introduces a unit error to the sig‐nal. The noise produced due to this unit error is imper‐ceptible. But anybody could omit the watermark without degrading the quality of the media by substituting the least significant bits with zero. As the LSB layer for em‐

bedding the secret bits increases, the error gets larger. For instance, hiding information in the second least signifi‐cant bit doubles the modification error. This larger error generates a more perceptible noise but since replacing this high level bit with zero degrades the quality of the signal, a more robust watermark is attained. In brief, if the watermark is imperceptibly embedded in a higher LSB

layer a stronger watermarking scheme is achieved [1, 2]. In the second part of the paper some related former

works are mentioned. In the third section the proposed

method and algorithm is explained. In the fourth section

experimental results are presented. And at the fifth part a

————————————————

Zahra Movahhedinia is with the Department of Computer Engineering, Isfahan University, Isfahan‐Iran.

Dr. Kamal Jamshidi is a faculty member of the Department of Computer Engineering, Isfahan University, Isfahan‐Iran.

D

JOURNAL OF COMPUTING, VOLUME 4, ISSUE 1, JANUARY 2012, ISSN 2151-9617

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conclusion is made. An appendix is attached to the end of the paper.

2 RELATED WORKS

A robust watermarking scheme is introduced in [5] which

promotes the limit for transparent data hiding in audio signals from the fourth LSB layer to the eighth LSB layer. This method puts a threshold on the signal by using noise gate software logic to avoid hiding data in the silent scopes of the signal.

In [6] a random mapping function is used to disorder the location of the covert pixels in the cover image. This method enhances the robustness and security of the wa‐termark. By using this technique, since the malicious at‐tacker does not know the correct order of the covert im‐

age, she cannot rebuild it. In addition, this technique is robust against cropping attack. Because the pixels of the covert image are not located in order, cutting part of the stego signal doesnʹt destroy the secret image partly and

just introduces an additive noise to it, thus the hidden

data can be recognizably detected.In [7] the robustness of LSB is promoted by the aid of

chaotic embedding. In this paper by experimenting addi‐tive noise attack as well as cropping attack on the pro‐posed method, its strength has been proven .

A discrete wavelet transform has been used in [8] to find the location where the covert bits can be embedded. After that the bits are embedded in their determined loca‐tion using LSB. In this method synchronic signals have been used for inserting and extracting the watermark. The method proposed in this paper is robust against resam‐

pling, equalization, cropping, and filtering attack. In [3, 4] a two‐step LSB‐ based watermarking scheme is

introduced which

hides

the

covert

bits

in

the

sixth

LSB

layer. In the first step, after inserting the secret bit into the cover sample, the sample is revised in order to gain the minimum possible error. In the second step, the noise is modified to reduce the distortion resulting in a more im‐

perceptible signal. The idea behind the first step is based

on this logic that if a 0 has to be inserted in the fourth LSB

layer of the cover sample (0…01000)2=(8)10 then for the sake of introducing a smaller error to the stego signal, the stego sample should be (0…00111)2=(7)10 rather than

(0…00000)2=(0)10. The algorithm presented in [3, 4] for the first step has been attached to our paper in appendix A. We have used this idea in our work after amending the algorithm. In our algorithm, since the negative numbers are represented by 2’complement method [10], the most significant bit which defines the sign of the number is not changed. The lines pertinent to this modification are bolded in the algorithm below. Other than that, the same procedure built for the positive numbers is used for the negative ones, as there is no need to change the procedure when 2ʹcomplement method is used for the negative numbers. Another case considered in our algorithm con‐

tains modifying the higher bits properly. For example, if bit 0 is to be embedded in the fourth LSB layer of (0…0011111)2=(31)10 , the stego sample ought to be (0…0100000)2=(32)10. But by following the algorithm pre‐

sented in [3, 4], (0…0110000)2=(48)10 is received. We’ve corrected the lines 6, 7, 8, 9 and 16, 17, 18, 19 to obtain this purpose. Overall, our amended algorithm is :

1. if we have ak=0 but ak=1 : 2. if ak‐1=0 : ak‐1=ak‐2=…=a0=1 3. else if ak‐1=1 : 4. ak‐1=ak‐2=…=a0=0 and

5. if ak+1=0 : ak+1=1 6. else if ak+2=0 : ak+2=1, ak+1=0 7. else if ak+3=0 : ak+3=1, ak+2=ak+1=0 8. …

9. else if a14=0 : a14=1, a13=…=ak+1=0 10. else ak‐1=ak‐2=…=a0=1

11. else if we have ak=1 but ak=0 : 12. if ak‐1=1 : ak‐1=ak‐2=…=a0=0 13. else if ak‐1=0 : 14. ak‐1=ak‐2=…=a0=1 and

15. if ak+1=1 : ak+1=0 16. else if ak+2=1 : ak+2=0, ak+1=1 17. else if ak+3=1 : ak+3=0, ak+2=ak+1=1 18. …

19. else if a14=1 : a14=0, a13=…=ak+1=1 20. else ak‐1=ak‐2=…=a0=0

In this algorithm, the normal bits (ak) are the cover bits and the underlined bits (ak) are the stego bits. Note that the watermark bit is inserted firstly and then the algo‐rithm presented above is executed. Consequently, if the embedded bit doesn’t change the original sample, the algorithm would not affect the sample. We insert the wa‐termark in the tenth LSB layer. If this algorithm hadn’t been used an error equal to 512 would have been pro‐duced, but by using this algorithm the error introduced is 256 at most and 1 at least. For instance, inserting 0 in the

tenth LSB layer of (0000001000000000)2=(512)10 results in (0000000111111111)2=(511)10 and produces an error equal to 512‐511=1. On the other hand, inserting 0 in the tenth LSB layer of (0000001100000000)2=(768)10 results in

(0000010000000000)2=(1024)10 and produces an error equal to 1024‐768=256.

3 THE PROPOSED ALGORITHM

We imperceptibly hide the watermark bits in the tenth LSB layer using the algorithm presented in the previous section. Our scheme is based on the idea that noise is less perceptible in regions where the energy of the signal is

higher. Experiments

on

human

auditory

system

(HAS)

verifies this fact. For example, it is well known that any kind of noise in the silent scopes of an audio file is very

annoying. This phenomenon can be explained by the sig‐nal to noise ratio. Keeping the noise energy constant, am‐

plifying the signal energy causes in a higher SNR. We use a chained hash table (CHT) in our algorithm to classify the energies of different regions of the cover signal while guarding the time complexity O(n). CHT is a data struc‐ture which the data elements of it may have identical keys. In the CHT shown in Fig. 1 all the elements in the same row have an identical key.






In our algorithm we first identify the regions which

have the maximum energy and then embed the water‐mark bits in those places. The following formula calcu‐

lates a relative value for energy on a window of M sam‐

ples of the signal.

∑ || (1)

Where |x(i)| is the absolute value of the signal at the sample with index n. n1 and n1+M‐1 are the indexes of two different samples which are M‐1 samples apart. Since we just want to compare the energies of different regions, we don’t need to calculate the actual amount of energy on the windows. Thus, to reduce the computations we use the relative amount of energy presented by formula 1. The largest value for the size of the windows M is the

number of samples in the cover data divided by the num‐

ber of watermark bits, as represented in formula 2.

(2)

Where n is the number of samples in the cover data and m is the number of watermark bits that should be

embedded. Since

our

algorithm

hides

one

bit

in

every

M

sample, if the mean value of the cover samples is small, we would need smaller Ms to have more high‐energy windows and consequently find more samples with high‐

er energies. On the other hand, when the variance of the cover samples is large, manifold windows would help us to extract the larger samples. Therefore, we design equa‐tion 3 for the size of the windows.

|| (3)

Where is the standard deviation and || is the abso‐lute value of the expected value. The size of the window

could be 1 at least. Thus equation 4 is achieved.

1 ||

||

1 (4)

Where we have :

∑

(5) Hence, we can have expression 6 for M1.

|| 1 (6)

We round the result to the floor. Therefor, formula 7 is achieved for M.

1 (7)

To classify the relative energies, we define a value Q

and divide the relative energy e by Q. The answer is a key K for the CHT. Thus, elements of the CHT with larger keys belong to the regions with greater energies. But by using this technique, we would not know the order of the elements with the same key. Ergo, the optimum state oc‐curs when only one element is assigned to every key of the CHT. Consequently, when the variance of the samples is smaller more stages is required for the CHT. According‐ly, the standard deviation of the samples would be a suit‐able choice for Q, as represented in expression 8.

(8)Therefor, for smaller energy variances we would have

smaller spaces between the stages in the CHT. To implement this algorithm we cycle on all the sam‐

ples of the cover data twice. On the first loop we calculate , and xmax to obtain M by formula 7 and Q by formula 8. And on the second loop we calculate the energies on

the windows by formula 1 and divide it by Q to obtain

the key for the CHT. While calculating formula 1, the sample which has the largest value among those M sam‐

ples is drawn out. The index of this sample will be saved

in the CHT, assigned to the key K. After this, using the algorithm explained in section two, the watermark is in‐

serted into the samples which their indexes have been stored in the CHT. Obviously, the insertion should start with the elements that have larger keys. Apparently, for inserting the watermark, we need a loop with length m, where m is the number of bits in the secret message. So the total time complexity would be 2 [11]. One of the advantages of our proposed algorithm is

that since the watermark bits are first hidden in the sam‐

ples with greater energy, the bits of the secret image are not inserted in order. As mentioned before this technique

enhances the

robustness

and

security

of

the

watermark

scheme. The malicious attacker would not be able to re‐ build the covert image unless she knows the correct order of the hidden bits. Other than that, the scheme would be more robust against cropping. In our work, similar to the work done at [7], instead of

hiding a text, the picture of that text has been hidden into the container. In this case, attacks would not ruin the wa‐termark by changing some of the hidden bits and their effect would appear as a noise on the detected image. In order to enhance the security and have a blind wa‐

termarking scheme, we’ve stored the indexes of the sam‐

ples which the watermark has been inserted into along

with

the

header

of

the

covert

image

in

a

data

file

and

used

the file as a key for detecting the watermark.

4 EXPERIMENTAL RESULTS

We have examined our proposed algorithm on 50 differ‐ent audio files. Our dataset contained jazz, rap, guitar, violin, piano, drum, flute, santur, female voice, and male voice. All the audio files use 16 bits for every sample and

are mono‐channelled and digitized using Pulse Code Modulation (PCM). The sizes of the audio files are several mega‐ bytes.

Fig. 2 shows the average SNR ratio for the proposed

Fig. 1. A Chained Hash Table (CHT).






tenth LSB layer and standard tenth LSB layer methods. The SNR value was calculated using formula 9 [5, 9].

∑

∑

(9)

Where x(i) are the samples in the cover data and y(i) are the samples in the stego data. In Fig. 2 the horizontal axis represents the amount of bits hidden in the cover audio signal. The sizes of the covert images were from 1 kB to 30 kBs. The vertical axis represents the average SNR

in dB. As you can see, our proposed method has im‐

proved the SNR up to more than 10 dBs.

We have tested Mean Opinion Score (MOS) on three different kinds of audio files by defining five‐point im‐

pairment scales

and

assigning

the

marks

5 to

impercepti

‐

ble, 4 to perceptible but not annoying, 3 to slightly annoy‐ing, 2 to annoying, and 1 to very annoying. The results are presented in table1.

TABLE 1MEAN OPINION SCORE FOR THREE DIFFERENT KINDS OF AUDIO

FILES

Audio File Genre Mean Opinion ScorePiano 4.9

Santur 5.0

Human Singing (Male Voice) 5.0

To examine

the

robustness

of

our

scheme

against

different attacks we inserted the picture in Fig. 3 in our audio files. This image is a bmp‐mono‐chrome picture. The size of the image is 20990 bytes. added white Gaussian noise (AWGN) to our stego sig‐

nals and calculated the average Bit Error Rate (BER) for different variances of the Gaussian noise. The results are shown in Fig. 4. In this picture the horizontal axis represent the variance of the noise and the vertical axis represents the BER. While BER for standard LSB is about 0.5, BER for our proposed scheme is about 0.01.

Then we detected the embedded pictures. One of the detected pictures for additive white Gaussian noise with

variance 1000 is shown in figure5.

We examined cropping attack on our proposed algo‐rithm. We replaced 500000 samples of the stego signal with zero. Fig. 6 shows the detected image watermarked by standard tenth LSB layer algorithm after the cropping attack and Fig. 7 shows the detected image watermarked

Fig. 2. Average SNR diagram for the proposed 10th

LSBlayer and standard 10

thLSB layer.

Fig. 3. The covert image

Fig. 4. BER for AWGN with Different variances.

Fig. 5. The image detected after adding WGN with variance1000 to the stego signal.






by our proposed tenth LSB layer method after the crop‐

ping attack.

As you can see, cropping attack doesn’t spoil the wa‐termarked image and just introduces some noise to it.

5 CONCLUSION

A robust LSB‐ based algorithm for digital watermarking has been proposed in this paper. Using the high energy regions of the signal, we’ve been able to hide the water‐mark bits in the tenth LSB layer. By means of a chained

hash table, we have been able to reach to a computational complexity lower than O(nm), where m is the number of bits in the covert image and n is the number of samples in

the container. We’ve applied AWGN and cropping attacks on the proposed scheme and successfully detected the embedded secret message to show that the developed method is stronger than standard LSB‐ based watermark‐ing algorithm. Moreover the imperceptibility of our me‐thod is shown to be better than standard tenth LSB layer scheme.

6 APPENDIX A

This is the algorithm presented in [3, 4] :

if host sample a>0

if bit 0 is to be embedded

if 0 then … 11 … 1

if 1 then … 00 … 0 and

if

0 then

1

else if 0 then 1

...

else if 0then 1

else if bit 1 is to be embedded

if 1 then … 00 … 0

if 0 then … 11 … 1 and

if 1 then 0

else if 1 then 0

...

else if 1 then 0

if host sample a<0

if

bit

0

is

to

be

embedded

if 0 then … 11 …1

if 1 then … 00 …0 and

if 1 then 0

else if 1 then 0

...

else if 1 then 0

else if bit 1 is to be embedded

if 1then … 00 … 0

if 0 then … 11 …1 and

if 1 then 0

else if 1 then 0

...

else if 1 then 0

REFERENCES

[1] Cvejic, N. and T. Seppanen, Digital Audio Watermarking Tech‐

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[2] N Verma, Mumbai Maharashtra, ʺReview of Steganography

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[5] Mohamed A. Ahmed, et al., ʺA Novel Embedding Method to

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marking Scheme with Random Mapping Function,ʺ IEEE, In‐

ternational Symposium on Ubiquitous Multimedia Computing, pp.

130‐134, 2008.

Fig. 6. The image detected Watermarked by standard tenthLSB layer algorithm after the cropping attack

Fig. 7. The detected image watermarked by our proposed 10th

LSB layer algorithm after cropping attack.






[7] Guang‐ming Yang, Yang Zhou, ʺLSB Algorithm Research Based

on Chaos,ʺ IEEE, Ninth International on Hybrid Intelligent Sys‐

tems, pp. 427‐430, 2009.

[8] Yin Xiong, Zhang Xiao Ming, ʺCovert Communication Audio

Watermarking Algorithm Based on LSB,ʺ IEEE, Communication

Technology, pp. 1‐4, 2006.

[9] Bassia, Pitas, Nikolaidis, “Robust audio watermarking in the

time domain,” IEEE, Transactions on Multimedia, pp. 232‐241,

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[10] M. Morris Mano, Digital Design , Prentice/Hall International,

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[11] Richard E. Neapolitan, Kumarss Naimipour, Foundations of

Algorithms Using Java Pseudocode , Jones and Bartlett publishers,

pp. 1‐46, 2004.

Zahra Movahhedinia Is a master student of computer engineeringat Isfahan University. She got her batchler degree on computer en-gineering-hardware at Isfahan University in 2010. She`s currently amaster student.

Dr. Kamal Jamshidi Is a faculty member of the Department of com-puter engineering at Isfahan University. He got his batchler degreeon electrical engineering at Isfahan University and his master degreeon Control and Instrumentation at Anna University in India. He gothis PHD on fuzzy control at I.I.T in India.




a novel approach for increasing robustness and security of lsb-based digital audio watermarking

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