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6th European Conference - Academic Conferences

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M<br />

M<br />

M<br />

Su−2 Sv<br />

∑∑<br />

u= 1 v=<br />

1<br />

h(,<br />

i j)<br />

=<br />

Su−1 Sv<br />

Su Sv−2<br />

∑∑<br />

u= 1 v=<br />

1<br />

Kesav Kancherla and Srinivas Mukkamala<br />

δ(<br />

Fh( u, v) = i, Fh( u+ 1, v) = j)<br />

∑∑<br />

u= 1 v=<br />

1<br />

v(,<br />

i j)<br />

=<br />

Su Sv−1<br />

Su−2 Sv−2<br />

∑ ∑<br />

u= 1 v=<br />

1<br />

δ(<br />

Fh( u, v) = i)<br />

δ(<br />

Fv( uv , ) = iF , v(<br />

uv , + 1) = j)<br />

∑∑<br />

u= 1 v=<br />

1<br />

d (, i j)<br />

=<br />

Su−1 Sv−1<br />

M<br />

Su−2 Sv−2<br />

∑∑<br />

u= 1 v=<br />

1<br />

δ(<br />

Fuv v(<br />

, ) = i)<br />

δ ( Fd( uv , ) = iF , d(<br />

u+ 1, v+ 1) = j)<br />

∑∑<br />

u= 1 v=<br />

1<br />

m(,<br />

i j)<br />

=<br />

Su−1 Sv−1<br />

u= 1 v=<br />

1<br />

δ ( Fd( u, v) = i)<br />

δ(<br />

Fm( u+ 1, v) = i, Fm( u, v+ 1) = j)<br />

∑∑<br />

δ(<br />

Fm( u, v) = i)<br />

Where Su and Sv are the dimensions of the image and δ (condition) = 1 if only if the conditions are<br />

satisfied. The final features will be the average of the above 4 transition matrix.<br />

5. Results<br />

We used 2000 images in our experiment. From these 2000 images we used 1400 images for training<br />

SVM and 600 images for testing. Each data point consists of 274 features, of which 193 are DCT<br />

features and 81 are Markov features. We used the following parameters for embedding data using<br />

YASS<br />

Three different quality factor modes 50/50, 50/75 and 75/75<br />

Four different block sizes 9, 10, 12 and 14<br />

Low frequency DCT coefficients used for embedding 12 (low) and 19 (high)<br />

We selected block sizes less than 14 as the block size increases the amount of data that can be<br />

embedded decreases. We choose the number coefficients used for embedding 19 because it is used<br />

in YASS (Solanki, Sarkar and Manjunath, 2007: 16-31) paper and value 12 to show the performance<br />

of our steganalysis scheme at low embedding rates. Table 1 and Table 2 give the accuracies<br />

obtained for different parameters at high data and low data respectively.<br />

Table 1: Accuracy obtained for different block sizes, compression rates and coefficients used equal to<br />

19<br />

Advertised-Design Compression rate/ Block Size 9 10 12 14<br />

50-50 99.8 99.7 99.75 99.7506<br />

50-75 97.1737 97.584 97.5894 96.0881<br />

75-75 97.5973 97.6725 97.0075 96.0881<br />

Table 2: Accuracy obtained for different block sizes, compression rates and coefficients used equal to<br />

12<br />

Advertised-Design Compression rate/ Block Size 9 10 12 14<br />

50-50 99.8337 99.5012 99.335 99.47<br />

50-75 96.5087 96.7581 96.84 95.59<br />

75-75 96.59 96.68 95.6775 94.55<br />

We obtained an accuracy of about 99.5 % for 50-50 setting even when we used only 12 coefficients<br />

for embedding. There is a decrease in accuracy as the block size increases for all compression<br />

setting. This is due to the fact that as size of the block increases the embedding capacity decreases.<br />

We obtained an accuracy of above 95% for all setting even when block size is 14 and embedding<br />

147<br />

(5)<br />

(6)<br />

(7)<br />

(8)

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