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Ta b l e 3 . BER under noise addition
Noise addition
Proposed
RDM-Basic10
RDM-Basic30
RDM-Basic50
Gaussian noise ( ˃ =10)
0.0323
0.1249
0.0942
0.0804
Gaussian noise ( ˃ =20)
0.2401
0.4344
0.4405
0.4400
Salt&Pepper ( p =0 . 01)
0.0512
0.0642
0.0662
0.0579
Salt&Pepper ( p =0 . 02)
0.0562
0.0660
0.0699
0.0608
Ta b l e 4 . BER under geometric attacks
Attacks
Proposed
RDM-Basic10
RDM-Basic30
RDM-Basic50
Rotation (0 . 5 )
0.3142
0.3367
0.3667
0.3629
0 . 5 )
Rotation (
0.3123
0.3511
0.3801
0.3763
Resizing (256 × 256)
0.0037
0.0079
0.0056
0.0050
Resizing (128 × 128)
0.0603
0.0814
0.0699
0.0676
Ta b l e 5 . BER comparison between the proposed method and MWT-EMD [13]
Attacks
Proposed
MWT-EMD
Attacks
Proposed
MWT-EMD
Median filtering (5 × 5)
0.0046
0.0975
JPEG (Q=10)
0
0
Median filtering (7 × 7)
0.0353
0.1094
JPEG (Q=20)
0
0
Rotation (1 . 0 )
Median filtering (9 × 9)
0.0896
0.6524
0.3250
0.5469
Rotation (0 . 5 )
Average filtering
(3 × 3)
0
-
0.1009
0.4492
Average filtering
(5 × 5)
0
-
Rotation
( 0 . 5 )
0.1110
0.4414
Gaussian filtering
(3 × 3)
0
0
Rotation
( 1 . 0 )
0.2873
0.5703
Gaussian filtering
(5 × 5)
0
0.0156
Salt&Pepper
( p =0 . 08)
0
0.0284
4.2
Comparison with Other Watermarking Methods
In order to further evaluate the performance of the improved RDM method, we also
compare it with two watermarking methods MWT-EMD [13] and GDWM [10]. MWT-
EMD is the state-of-the-art method in spread spectrum watermarking and GDWM is
one of the state-of-the-art methods in quantization-based watermarking.
Table 5 compares the BER results of the improved RDM method with MWT-EMD
method. As in [13], the test images are ”Baboon,” ”Goldhill,” ”Lena,” and ”Pepper”
and a 64-bit message is embedded in each image with the PSNR of about 42dB. The
results of our method are the averaged BERs obtained from embedding 100 different
watermarks in each image. It can be seen that our method outperforms MWT-EMD
under all the considered attacks.
Table 6 compares the BER results of the improved RDM method with GDWM
method. As in [10], the test images are ”Baboon,” ”Barbara,” ”Lena,” and ”Pepper” and
a 256-bit message is embedded in each image with the PSNR of 43.29dB, 42.70dB,
43.54dB and 43.06dB respectively. We can see that our method is more robust than
GDWM in general.
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