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[WM]: Questions about Harmsen's steganalytic algorithms.
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Hi,I did Harmsen's experiments according to the paper "Steganalysis of =
additive noise modelable information hiding".I have
implemented Harmsen's algorithms.However,I get the completely inverse =
results comparing to his using the same 24 png images and
cox's SSIS steganographic method.Meanwhile,after I realized Harmsen's =
another paper "Fast Additive Noise Steganalysis",I found the
results fit to Harmsen's rule given in the first paper.That is "As =
expected the centers of mass for the stegoimages are considerably
lower than those of the originals." Therefore,I am confused about these =
results.<p><p>Maybe,who have also realized the algorithms.I
hope you can tell me why.<p><p><p>Thanks!<p>listle.<p><p>The cover =
images are "kodak PhotoCD PCD0992" come from
"http://r0k.us/graphics/kodak/".They are 24 PNG images.The =
steganographic algorithm is Cox's SSIS steganography. I set N =3D 1000 =
and
alpha =3D 0.1.Using above algorithm,I get 48 groups of the COM =
vector(three dimensions) aiming to co!
ver images and stego images.<p><p>1.The two groups of results obtained =
according to Harmsen's first paper list below.<p><p>COM
vectors for 24 cover images<p>----------------------<p>63.48 67.33 =
66.642<p>61.503 67.69 67.019<p>65.94 67.559
66.409<p>62.862 66.58 65.275<p>63.987 66.568 65.667<p>63.305 66.176 =
55.099<p>63.755 66.643 66.666<p>64.534 65.253
64.29<p>63.317 67.175 66.002<p>63.417 66.991 65.299<p>68.581 68.52 =
67.323<p>62.898 67.222 65.6<p>64.103 65.734
63.326<p>64.476 66.137 64.638<p>64.137 67.23 66.494<p>63.857 68.412 =
66.117<p>63.298 68.33 64.64<p>64.214 67.826
65.915<p>64.324 69.961 66.941<p>63.666 63.711 60.177<p>63.877 68.381 =
65.342<p>62.928 68.89 66.798<p>63.357 67.694
66.382<p>63.459 63.698 63.288<p>----------------------<p><p>COM vectors =
for 24 stego images<p>----------------------<p>76.823
74.821 77.339<p>78.204 75.962 80.547<p>76.592 76.453 77.088<p>72.383 =
73.65 74.694<p>73.333 74.225 74.899<p>74.187 73.39
70.955<p>77.579 75.954 79.147<p>66.41 65.984 65.874<p!
>75.387 75.473 79.598<p>72.552 73.017 72.433<p>80.276 81.115 80.254<p>
74.52 73.021 71.105<p>69.664 69.149 67.406<p>74.565 72.459 =
75.544<p>64.094 65.282 65.086<p>76.069 75.853 76.977<p>73.651
80.126 77.687<p>73.166 74.102 73.238<p>75.504 75.208 7
322<p>66.816 66.812 65.422<p>75.297 76.034 75.621<p>70.91 71.511 =
73.706<p>68.895 69.256 70.69<p>71.667 71.509
71.608<p>----------------------<p><p><p><p>2. The two groups of results =
obtained according to Harmsen's second paper list
below.<p><p>When I calculate r,g and b color channel respectively,I got =
the results.<p><p>COM vectors for 24 cover
images<p>----------------------<p>31.258 25.523 24.58<p>28.35 18.461 =
24.383<p>30.169 30.534 29.618<p>25.102 19.144
26.177<p>28.892 27.925 27.249<p>55.329 54.41 25.838<p>33.438 29.694 =
23.616<p>48.754 44.99 40.158<p>30.609 25.342
24.96<p>29.551 24.9 23.815<p>34.999 32.21 32.593<p>28.716 41.813 =
24.885<p>40.674 42.828 24.702<p>34.059 28.075 27.60
<p>37.877 58.524 29.521<p>28.588 28.073 30.197<p>34.974 23.948 =
27.008<p>31.286 17.179 14.797<p>31.83 15.883
12.224<p>62.94 62.488 58.132<p>34.232 24.909 13.519<p>42.821 13.068 =
12.415<p>33.611 17.335 28.843<p>53.711 53.078
48.234<p>----------------------<p><p>COM vectors for 24 stego =
images<p>----------------------<p>6.5947 7.827 7.0537<p>7.3086
11.35 9.4164<p>13.509 11.625 10.765<p>7.2636 8.4345 7.3664<p>15.254 =
16.214 8.0917<p>16.379 16.046 7.9064<p>8.3979 8.6236
8.1875<p>35.255 33.264 34.528<p>7.4386 7.761 7.67<p>15.678 16.475 =
8.7332<p>8.555 8.1086 8.5541<p>21.199 38.805
7.0095<p>26.565 29.468 9.0519<p>9.1548 7.6585 8.074<p>33.633 52.771 =
31.01<p>8.9264 8.8929 8.4551<p>9.2521 8.2303
9.5923<p>11.111 7.7103 6.4926<p>16.463 7.2212 6.9121<p>47.605 46.491 =
47.031<p>13.532 11.845 8.2464<p>20.688 7.2306
6.6256<p>33.271 12.482 9.8739<p>28.896 25.082 =
20.086<p>----------------------<p><p><p>3. When I calculate co-occur =
matrix of
rg,gb and br color channels respectively,I got the resul!
ts.<p>COM vectors for 24 cover images<p>----------------------<p>63.069 =
59.947 62.558 65.013 57.435 63.838<p>59.018
65.358 58.439 66.503 43.963 52.284<p>67.706 65.238 67.54 67.302 63
89 68.979<p>63.896 63.052 64.113 64.789 57.455 62.047<p>63.222 63.301 =
64.107 64.111 60.78 65.568<p>63.475 63.027
64.074 52.459 61.458 54.227<p>65.698 65.293 65.318 65.791 63.05 =
70.15<p>62.835 62.497 62.755 62.016 62.428
62.64<p>68.079 65.725 64.849 65.814 58.516 68.08<p>64.064 62.552 64.069 =
63.397 59.102 67.356<p>67.347 66.797 63.696
67.903 58.805 63.429<p>64.175 65.529 66.013 66.365 57.648 =
64.024<p>62.678 62.413 63.044 60.924 61.441 61.244<p>65.698
61.472 62.96 64.442 60.629 67.542<p>55.41 66.232 64.71 66.383 62.348 =
55.186<p>66.996 65.907 62.908 65.046 59.92
69.142<p>64.443 63.358 62.958 64.184 50.939 62.734<p>66.892 57.771 =
66.222 64.054 60.259 67.869<p>68.605 63.89 67.401
64.785 62.016 69.146<p>63.504 62.99 63.524 59.623 63.547 60.025<p>65.578 =
60.216 63.869 62.421 54.18 65.726<p>67.818
55.878 62.973 56.805 54.551 67.706<p>68.261 64.163 59.991 59.532 55.115 =
67.284<p>63.375 63.267 63.246 62.877 63.162
62.97<p>----------------------<p><p>COM vectors for 24 stego =
images<p>----!
------------------<p>52.64 52.903 55.413 55.875 48.59 47.502<p>33.053 =
38.349 56.008 56.193 27.494 25.523<p>57.886
57.731 59.519 58.914 55.784 55.948<p>53.905 55.266 59.528 58.611 50.311 =
50.585<p>59.023 60.05 56.004 54.542 54.246
52.491<p>60.722 60.706 51.711 47.621 48.851 45.774<p>53.483 53.067 =
56.271 57.798 49.195 50.903<p>61.845 61.953 61.587
61.176 61.034 61.554<p>53.654 53.856 57.178 57.866 48.35 50.152<p>59.436 =
59.509 57.926 56.043 53.109 52.156<p>53.425
53.163 54.615 54.351 49.865 50.908<p>57.118 61.013 59.158 47.709 57.304 =
33.607<p>61.117 61.902 59.264 54.86 58.917
52.461<p>58.382 56.726 58.025 57.996 54.72 56.271<p>55.785 62.914 61.35 =
62.213 63.095 58.876<p>59.651 59.848 58.619
58.68 56.329 58.836<p>50.177 50.356 53.048 54.774 42.777 44.487<p>55.514 =
55.165 52.283 52.798 49.825 51.094<p>57.049
52.412 59.256 55.581 51.159 52.456<p>63.08 62.726 62.853 61.318 63.011 =
61.702<p>55.042 55.099 44.052 44.002 33.049
33.73<p>53.683 50.138 53.119 50.143 49.598 51.441<p>60.089 54!
.444 58.743 52.853 53.558 54.749<p>61.998 61.691 61.59 61.028 61.466 6
1.116<p>----------------------<p>
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<html>
<head>
<meta http-equiv=3D"Content-Type" content=3D"text/html; =
charset=3DGB2312"></head> <body> <!--SOHUMAIL_HTML_HEAD_END-->Hi,I did =
Harmsen's
experiments according to the paper "Steganalysis of additive noise =
modelable information hiding".I have implemented Harmsen's
algorithms.However,I get the completely inverse results comparing to his =
using the same 24 png images and cox's SSIS steganographic
method.Meanwhile,after I realized Harmsen's another paper "Fast Additive =
Noise Steganalysis",I found the results fit to Harmsen's
rule given in the first paper.That is "As expected the centers of mass =
for the stegoimages are considerably lower than those of the
originals." Therefore,I am confused about these results.<p><p>Maybe,who =
have also realized the algorithms.I hope you can tell me
why.<p><p><p>Thanks!<p>listle.<p><p>The cover images are "kodak PhotoCD =
PCD0992" come from "http://r0k.us/graphics/kodak/".They are
24 PNG images.The steganographic algorithm is Cox's SSIS steganography. =
I set N =3D 1000 and alpha =3D 0.1.Using above algorithm,I get
48 groups of the COM vector(t!
hree dimensions) aiming to cover images and stego images.<p><p>1.The =
two groups of results obtained according to Harmsen's first
paper list below.<p><p>COM vectors for 24 cover =
images<p>----------------------<p>63.48 67.33 66.642<p>61.503 67.69
67.019<p>65.94 67.559 66.409<p>62.862 66.58 65.275<p>63.987 66.568 =
65.667<p>63.305 66.176 55.099<p>63.755 66.643
66.666<p>64.534 65.253 64.29<p>63.317 67.175 66.002<p>63.417 66.991 =
65.299<p>68.581 68.52 67.323<p>62.898 67.222
65.6<p>64.103 65.734 63.326<p>64.476 66.137 64.638<p>64.137 67.23 =
66.494<p>63.857 68.412 66.117<p>63.298 68.33
64.64<p>64.214 67.826 65.915<p>64.324 69.961 66.941<p>63.666 63.711 =
60.177<p>63.877 68.381 65.342<p>62.928 68.89
66.798<p>63.357 67.694 66.382<p>63.459 63.698 =
63.288<p>----------------------<p><p>COM vectors for 24 stego
images<p>----------------------<p>76.823 74.821 77.339<p>78.204 75.962 =
80.547<p>76.592 76.453 77.088<p>72.383 73.65
74.694<p>73.333 74.225 74.899<p>74.187 73.39 70.955<p>77.579 75.954 7!
9.147<p>66.41 65.984 65.874<p>75.387 75.473 79.598<p>72.552 73.017 72.
433<p>80.276 81.115 80.254<p>74.52 73.021 71.105<p>69.664 69.149 =
67.406<p>74.565 72.459 75.544<p>64.094 65.282
65.086<p>76.069 75.853 76.977<p>73.651 80.126 77.687<p>73.166 74.102 =
73.238<p>75.504 75.208 77.322<p>66.816 66.812
65.422<p>75.297 76.034 75.621<p>70.91 71.511 73.706<p>68.895 69.256 =
70.69<p>71.667 71.509
71.608<p>----------------------<p><p><p><p>2. The two groups of results =
obtained according to Harmsen's second paper list
below.<p><p>When I calculate r,g and b color channel respectively,I got =
the results.<p><p>COM vectors for 24 cover
images<p>----------------------<p>31.258 25.523 24.58<p>28.35 18.461 =
24.383<p>30.169 30.534 29.618<p>25.102 19.144
26.177<p>28.892 27.925 27.249<p>55.329 54.41 25.838<p>33.438 29.694 =
23.616<p>48.754 44.99 40.158<p>30.609 25.342
24.96<p>29.551 24.9 23.815<p>34.999 32.21 32.593<p>28.716 41.813 =
24.885<p>40.674 42.828 24.702<p>34.059 28.075
27.602<p>37.877 58.524 29.521<p>28.588 28.073 30.197<p>34.974 23.948 =
27.008<p>31.286 17.179 14.7!
97<p>31.83 15.883 12.224<p>62.94 62.488 58.132<p>34.232 24.909 =
13.519<p>42.821 13.068 12.415<p>33.611 17.335
28.843<p>53.711 53.078 48.234<p>----------------------<p><p>COM vectors =
for 24 stego images<p>----------------------<p>6.5947
7.827 7.0537<p>7.3086 11.35 9.4164<p>13.509 11.625 10.765<p>7.2636 =
8.4345 7.3664<p>15.254 16.214 8.0917<p>16.379 16.046
7.9064<p>8.3979 8.6236 8.1875<p>35.255 33.264 34.528<p>7.4386 7.761 =
7.67<p>15.678 16.475 8.7332<p>8.555 8.1086
8.5541<p>21.199 38.805 7.0095<p>26.565 29.468 9.0519<p>9.1548 7.6585 =
8.074<p>33.633 52.771 31.01<p>8.9264 8.8929
8.4551<p>9.2521 8.2303 9.5923<p>11.111 7.7103 6.4926<p>16.463 7.2212 =
6.9121<p>47.605 46.491 47.031<p>13.532 11.845
8.2464<p>20.688 7.2306 6.6256<p>33.271 12.482 9.8739<p>28.896 25.
82 20.086<p>----------------------<p><p><p>3. When I calculate co-occur =
matrix of rg,gb and br color channels respectively,I
got the results.<p>COM vectors for 24 cover =
images<p>----------------------<p>63.069 59.947 62.558 65.013 57.435
63.838<p>59.018 65.358 58.439 66.503 43.963 52.284<p>
67.706 65.238 67.54 67.302 63.89 68.979<p>63.896 63.052 64.113 64.789 =
57.455 62.047<p>63.222 63.301 64.107 64.111
60.78 65.568<p>63.475 63.027 64.074 52.459 61.458 54.227<p>65.698 65.293 =
65.318 65.791 63.05 70.15<p>62.835 62.497
62.755 62.016 62.428 62.64<p>68.079 65.725 64.849 65.814 58.516 =
68.08<p>64.064 62.552 64.069 63.397 59.102
67.356<p>67.347 66.797 63.696 67.903 58.805 63.429<p>64.175 65.529 =
66.013 66.365 57.648 64.024<p>62.678 62.413 63.044
60.924 61.441 61.244<p>65.698 61.472 62.96 64.442 60.629 67.542<p>55.41 =
66.232 64.71 66.383 62.348 55.186<p>66.996
65.907 62.908 65.046 59.92 69.142<p>64.443 63.358 62.958 64.184 50.939 =
62.734<p>66.892 57.771 66.222 64.054 60.259
67.869<p>68.605 63.89 67.401 64.785 62.016 69.146<p>63.504 62.99 63.524 =
59.623 63.547 60.025<p>65.578 60.216 63.869
62.421 54.18 65.726<p>67.818 55.878 62.973 56.805 54.551 67.706<p>68.261 =
64.163 59.991 59.532 55.115 67.284<p>63.375
63.267 63.246 62.877 63.162 62.97<p>----------------------<p><p>COM =
vect!
ors for 24 stego images<p>----------------------<p>52.64 52.903 55.413 =
55.875 48.59 47.502<p>33.053 38.349 56.008
56.193 27.494 25.523<p>57.886 57.731 59.519 58.914 55.784 =
55.948<p>53.905 55.266 59.528 58.611 50.311 50.585<p>59.023
60.05 56.004 54.542 54.246 52.491<p>60.722 60.706 51.711 47.621 48.851 =
45.774<p>53.483 53.067 56.271 57.798 49.195
50.903<p>61.845 61.953 61.587 61.176 61.034 61.554<p>53.654 53.856 =
57.178 57.866 48.35 50.152<p>59.436 59.509 57.926
56.043 53.109 52.156<p>53.425 53.163 54.615 54.351 49.865 =
50.908<p>57.118 61.013 59.158 47.709 57.304 33.607<p>61.117
61.902 59.264 54.86 58.917 52.461<p>58.382 56.726 58.025 57.996 54.72 =
56.271<p>55.785 62.914 61.35 62.213 63.095
58.876<p>59.651 59.848 58.619 58.68 56.329 58.836<p>50.177 50.356 53.048 =
54.774 42.777 44.487<p>55.514 55.165 52.283
52.798 49.825 51.094<p>57.049 52.412 59.256 55.581 51.159 52.456<p>63.08 =
62.726 62.853 61.318 63.011 61.702<p>55.042
55.099 44.052 44.002 33.049 33.73<p>53.683 50.138 53.119 50!
.143 49.598 51.441<p>60.089 54.444 58.743 52.853 53.558 54.749<p>61.99
8 61.691 61.59 61.028 61.466 61.116<p>----------------------<p><hr =
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