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CHAPTER 7

PERFORMANCE EVALUATION

7.1 INTRODUCTION

The efficiency of any system can be defined only based on certain parameters

which describe the behavior of the developed system towards some forced and unforced

conditions. For any data hiding system, the performance is described in terms of

robustness, visual imperceptibility and embedding capacity. They are determined by

exposing the embedded media towards a wide range of intentional and unintentional

attacks. Numerical representations of the performance are expressed in terms of PSNR,

CC, SSIM and BER as already discussed in the previous chapters. This chapter is

organized as follows.

The general scheme of evaluation is discussed in section 7.2 which outlines

the different parameters measured at different stages of the entire process.

Section 7.3 presents a systematic report on the robustness and

imperceptibility measurements after being exposed to a wide range of

attacks.

A comparative analysis of the existing system with the conventional data

hiding techniques like DCT, DWT for the same image under study is

illustrated in section 7.4.

The last part of evaluation involves testing the effect of payload increase

over the integrated data hiding system which is consolidated in section 7.5.

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Based on the evaluation results, a summary of this chapter is drawn in

section 7.6.

7.2 EVALUATION SCHEME

The general evaluation scheme adopted for estimating the performance of

the proposed integrated data hiding system is illustrated in figure 7.1. It can be seen from

the illustration that three critical metrics are evaluated namely the PSNR, CC and SSIM.

While PSNR defines the content of signal over noise, CC and SSIM define the

resemblance of extracted image after being attacked to the original image before

embedding. While PSNR follows a decibel scale, CC and SSIM are real numbers

graduated from 0 to 1.

Figure 7.1 Scheme for robustness evaluation of the proposed technique

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In the above scheme of evaluation, PSNR are used at places to evaluate the

robustness of extracted data especially the watermarks to check for any tampering of data

during the transmission phase. The other metrics such as SSIM and CC are used at places

to test the visual resemblance of extracted cover image towards the original. Since,

medical images are the prime focus, any visual distortion on the medical cover image

cannot be tolerated and hence SSIM and CC are quite critical.

7.3 ROBUSTNESS AND IMPERCEPTIBILITY EVALUATION

The embedded image after being subjected to a wide range of aggressive

image processing operations is evaluated for its PSNR, CC and SSIM. While PSNR

defines the robustness more precisely, CC and SSIM are used to depict the degree of

resemblance of attacked and extracted image to the original image before embedding.

Weaker the embedding algorithm, more the visual distortions on the extracted data,

which is reflected on the CC and SSIM values. The tests have been done with a data base

of 8 images. The visual results of the attacks are shown with a knee MRI while others are

depicted using numerical values. The results are consolidated from attacks like noise,

filtering, rotation, scaling and blurring, image degradation attacks etc.,

7.3.1 Robustness towards Noise

The embedded knee MRI is exposed to noise with varying intensity values

of 0.02, 0.03, 0.08 and 0.30 and extracted using the reverse process explained in the

previous chapters. Its robustness is evaluated in terms of PSNR for the cover image and

correlation coefficient (CC) for the watermarks and tabulated in table 7.1. It illustrates the

robustness of the extracted cover image using PSNR and high values exceeding 45 dB are

reported which is much desirable in case of medical images. Further, the resemblance of

the extracted authentication payloads after exposure to noise of different intensities

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indicate a good fidelity value and degradation observed under high values of noise

addition exceeding 0.20 intensity value.

Table7.1. Evaluation of Robustness towards Noise

Attack Embedded

Image

Extracted Cover

Image

Watermark1 Watermark2

Noise

(0.02)

PSNR = 49.52 dB

CC = 0.9766

CC = 0.9896

Noise

(0.03)

PSNR = 47.52 dB

CC = 0.9564

CC = 0.9754

Noise

(0.08)

PSNR = 41.02 dB

CC = 0.8102

CC = 0.9122

Noise

(0.30)

PSNR = 36.25 dB

CC = 0.6956

CC = 0.8453

As mentioned in previous sections, 7 more image samples are tested for all the

attacks and performances evaluated. The addition of noise over a brain MR and CT is

shown in figure 7.2

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Figure 7.2 MR and CT brain images attacked by noise

7.3.2 Robustness towards Filtering attacks

The same knee MRI image which is embedded with the watermarks is now

subjected to wide range of image processing operations and its robustness evaluated in

terms of the performance metrics which has been systematically outlined below. Figure

7.3 illustrates the embedded image being exposed to filtering attacks namely low pass

filtering, high pass filtering, Weiner filtering and median filtering.

(a) (b)

(c) (d)

Figure 7.3 Attacked Knee MR Image a. Low Pass Filtering b. High Pass Filtering

c. Median Filtering d. Wiener Filtering

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Table 7.2 depicts the evaluation results of the embedded image towards filtering

attacks in terms of SSIM and CC. It can be seen that the proposed technique maintains a

high level of resemblance towards the original set of images and at the same time with a

high signal to noise ratio.

Table7.2. Evaluation of robustness and fidelity towards filtering – Brain MRI

Attack

Cover Image Watermark 1

(CC)

Watermark 2

(CC) SSIM PSNR (dB)

Low pass filtering 0.9352 52.054 0.8692 0.8894

High pass filtering 0.9425 52.855 0.8821 0.8801

Median filtering 0.9751 52.146 0.9100 0.9241

Weiner filtering 0.9844 52.478 0.9248 0.9421

Figures 7.4 illustrates the visual results of an attacked MR and CT image by low

pass and high pass filtering. Low pass filtering results in smoothening while high pass

filtering results in image sharpening.

Figure 7.4 Filtered brain MR and CT images – Low pass

and High pass filtering.

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The MR and CT images of brain attacked through median and wiener filtering are

depicted in figure 7.5

Figure 7.5 Filtered brain MR and CT images –median

and wiener filtering

Table 7.3 consolidates the evaluation results of the embedded image towards

filtering attacks in terms of SSIM and CC for a brain CT. Since, brain CT images have a

significantly higher number of high energy areas than knee MR images, a marginal

increase in the signal strength and fidelity is observed.

Table7.3. Evaluation of robustness and fidelity towards filtering – Brain CT image

Attack Cover Image Watermark 1

(CC)

Watermark 2

(CC) SSIM PSNR (dB)

Low pass filtering 0.9600 54.121 0.8901 0.9014

High pass filtering 0.9714 53.941 0.8900 0.8914

Median filtering 0.9698 53.104 0.8881 0.8874

Weiner filtering 0.9841 52.971 0.8471 0.8141

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7.3.3 Robustness towards Rotation and Scaling attacks

Figure 7.6 illustrates the embedded image towards rotation, scaling and cropping

attacks which try to change the geometry of the images. These are commonly known as

geometric attacks.

(a) (b)

(c) (d)

Figure 7.6 Attacked MR knee image a. scaled (12%) b. rotated image (450) c. cropped

image d. skewed image

Table 7.4 consolidates the results of robustness of the extracted images and watermarks

towards RST attacks in terms of PSNR and CC respectively.

Table7.4. Evaluation of robustness and fidelity towards RST attacks

Attacks

Cover Image Watermark 1

(CC)

Watermark 2

(CC)

SSIM

PSNR (dB)

Rotation (450) 0.8462 54.24 0.8652 0.8444

Scaling 0.9120 54.26 0.9854 0.8952

Blurring (Horizontal) 0.9329 53.78 0.8426 0.8471

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7.3.4 Robustness towards Image Degradation attacks

Image degradation attacks could be blurring, slicing, contrast stretching, gamma

correction and contrast enhancement. The behavior towards each of the above attacks is

illustrated in figure 7.7.

(a) (b)

(c) (d)

Figure 7.7 Attacked MRI knee image a. contrast enhanced image b. sliced image

c. contrast stretched image d. blurred image

7.4 COMPARATIVE EVALUATION OF ROBUSTNESS

It can be seen from the previous sections that the embedded image has been

subjected to wide range of aggressive image processing operations in an attempt to

simulate the real time intentional and unintentional attacks. The robustness evaluation

results are compared with other prominent techniques in Spatial and Frequency domain

techniques to justify the superiority behind the directional properties of the contourlet

transform and the robustness properties of discrete cosine transform and singular value

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decomposition. The extracted cover image has been evaluated in terms of PSNR, CC and

SSIM.

7.4.1 Robustness Comparison towards Noise

From figure 7.8 it can be seen that Contourlet transform in its hybrid combination

provides marginal improvement in resistance towards noise attacks with its frequency

domain counterparts.

Figure 7.8 Robustness comparison towards Noise

The extracted cover images are seen approaching a Peak Signal to Noise

ratios of 55dB even when more than one watermark is being embedded into it. This

resistance in terms of image quality is a desirable feature especially with medical images.

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7.4.2 Robustness comparison towards Rotation (450)

Figure 7.9 illustrates the behavior of each of extracted cover images

towards rotation attacks emphasizing the importance of singular value decomposition

providing the necessary rotation, scaling and translation invariance to the cover image. It

is evident from figure 7.9 that the proposed hybrid technique outperforms the other

existing techniques in its individual form towards rotation attacks. This justifies the

utilization of invariance properties of the SVD.

Figure 7.9 Robustness comparisons towards rotation through 450

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7.4.3 Robustness comparison towards filtering attacks

The robustness comparison between the different techniques with respect to their

resistance towards filtering attacks are shown in table 7.5 and it can be seen that hybrid

Contourlet transform is able to steep over the other counterparts.

Table 7.5 Robustness comparison towards filtering attacks (PSNR)

Attacks Input Cover

Image

Spatial

Domain

(dB)

Frequency Domain

DCT

(dB)

DWT

(dB)

Hybrid

Contourlet

(dB)

Lo

w P

ass

Fil

teri

ng

MRI Brain (Axial) 30.12 41.25 47.25 51.25

MRI Brain (Sagittal) 32.32 40.25 44.56 50.85

MRI Knee 30.99 42.44 43.69 53.14

CT Brain 33.69 43.24 46.25 55.24

Hig

h P

ass

Fil

teri

ng

MRI Brain (Axial) 31.23 40.14 42.88 51.24

MRI Brain (Sagittal) 34.25 39.25 42.14 51.02

MRI Knee 33.69 41.22 44.91 49.88

CT Brain 30.29 42.47 45.28 50.14

Med

ian

Fil

teri

ng

MRI Brain (Axial) 38.12 43.54 45.17 52.56

MRI Brain (Sagittal) 37.63 41.25 46.28 50.25

MRI Knee 35.25 46.25 49.44 55.35

CT Brain 34.25 41.25 44.17 51.48

Wei

ner

Fil

teri

ng

MRI Brain (Axial) 37.25 40.85 43.17 50.90

MRI Brain (Sagittal) 35.15 44.58 45.17 55.69

MRI Knee 39.56 44.17 49.69 55.18

CT Brain 38.25 43.22 48.54 54.33

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7.4.4 Robustness comparison towards compression

The robustness comparison between the proposed technique and its wavelet

counterpart towards JPEG compression attacks with different quality factors are shown in

figure 7.10 which clearly exhibits the superiority of the proposed technique towards other

techniques for increasing levels of quality factors. It is evaluated with respect to

correlation coefficient.

Figure 7.10 Robustness towards JPEG compression attacks.

7.5 EVALUATION OF EMBEDDING CAPACITY

An essential part of the proposed technique is to address the embedding

capacity increase with no compromise at the cost of robustness and visual

imperceptibility which is more prevalent with the existing techniques as discussed in the

literature survey. The patient information in the form of an AES encrypted EPR is

interleaved into the smooth regions of the cover image using modified histogram based

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DE. The embedded image along with the authentication payloads and the EPR are

attacked through several aggressive image processing operations as dealt in the previous

sections and the extracted EPR is evaluated in terms of a metric namely the bit error rate

(BER). It represents the number of bits received without any error. Prior to interleaving

the bits of EPR into the smooth regions, the bits are compressed using a run length

coding technique. The evaluation results are tabulated in table 7.6.

Table7.6. Performance of payload increase towards fidelity

Payload Original

bit length

(bits)

Bit length after

compression

(bits)

Compression

ratio

(%)

Correlation

coefficient

Payload 1 38500 37421 97.19 0.9898

Payload 2 51425 50140 97.50 0.9420

Payload 3 71254 70142 98.43 0.9101

Payload4 86524 85015 98.25 0.8785

Payload 5 100244 99412 99.17 0.8100

Payload 6 112104 101425 90.47 0.7856

Payload 7 120542 112045 92.95 0.6926

Payload 8 1274828 110235 86.47 0.6550

Figure 7.11 depicts the effect of bit rate in bits per pixel over the peak signal to

noise ratio and it is evident that both MR and CT images tested show a consistent and

very gradual decrease in PSNR with increasing bit rate.

Figure 7.11 Effect of increasing Bit length over PSNR

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Table 7.7 consolidates the effect of various attacks over the interleaved bits in

terms of bit error rate. It is necessary to monitor the bit error rate as it denotes the number

of bit flips as a result of attacks. The attacks included are noise, filtering and RST attacks.

Impulse noise with density of 0.05 and speckle noise of zero mean and variances 0.04,

have been used on the embedded image and a reasonably good BER is maintained

comparative to the existing techniques.

Table7.7. Effect of attacks on bit error rate

Type of

Attack

Noise Median

Filtering

Weiner

Filtering

Scaling

(12%)

Rotation

(450)

Bit Error

Rate (%)

4.89 3.44 3.20 6.49 28.6

Table 7.8 summarizes the consolidated evaluation results of the performance of

the proposed techniques with the other techniques implemented using spatial domain,

DCT, DWT, hybrid DWT and CT. It could be easily understood from the tabulation that

the proposed techniques provides better results for a fixed payload of 100248 bits. A

payload of 100248 bits consists most of the essential information required for labeling a

patient record like the name, age, sex, diagnosis, treatment schedule, hospital and

patient‘s address details. The essential contribution in this work is justified by the fact

that unlike the existing techniques, the proposed technique provides good signal quality

in terms of PSNR and at the same time good fidelity for increasing payloads in terms of

CC and SSIM.

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Table7.8. Consolidated performance analysis between data hiding techniques

Table 7.8 illustrates the fact that the proposed hybrid Contourlet transform

technique is able to maintain a good PSNR value for a sufficiently large payload of

100248. The relatively high performance of the proposed technique is attributed to the

fact that a proper balance is established between the three key embedding criteria. A large

number of high frequency sub bands of the Contourlet transform facilitate embedding

multiple payloads unlike its counterparts, and the energy compaction properties of the

discrete cosine transform provide good robustness to the embedded data while

interleaving information bits in the smooth regions of the image contribute to the

recorded high embedding capacity beyond which the quality of host image degrades

which could be understood from table 7.7

7.6 SUMMARY

A complete analysis of the proposed technique and its performance comparison

towards the other techniques are illustrated in this chapter. The embedded image using

the proposed technique is exposed to a systematic degradation and destruction

mechanism through a large number of aggressive image processing operations ranging

from noise to translation attacks, compression to blurring, filtering to cropping etc., and

S.

No.

Technique Authentication

Payloads

EPR (bits) PSNR

(dB)

CC SSIM

1. Luminance

based 2 100248 32.54 0.7412 0.7398

2. DCT 2 100248 36.78 0.8141 0.8101

3. DWT 2 100248 42.14 0.8444 0.8458

3. DCT – DWT 2 100248 45.29 0.8601 0.8699

4. CT - DCT 2 100248 48.77 0.8889 0.8841

5. Proposed

Technique 2 100248 53.41 0.9201 0.9184

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the performance analyzed in all possible metrics namely the PSNR, CC, MSE and SSIM

thereby presenting a clear picture of the efficiency of the proposed technique over the

existing ones. Visual and numerical results have been presented and the methodology of

the evaluation process illustrated in the earlier sections of this chapter. In spite of the fact

that this system might appear to be quite complex, it could be compensated with the fact

that their efficiency is very much higher and much required as the images under study are

medical images which are quite very sensitive and highly vital. The following chapter

provides the conclusion of the work and the future prospects.