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jessica FRIDRICH jan KODOVSK Ý miroslav GOLJAN vojt ě ch HOLUB

Breaking HUGO with four features. jessica FRIDRICH jan KODOVSK Ý miroslav GOLJAN vojt ě ch HOLUB. HUGO “preserves” the joint distribution of differences among four neighboring pixels. Thus, looking at the histogram of absolute differences between neighboring pixels makes no sense.

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jessica FRIDRICH jan KODOVSK Ý miroslav GOLJAN vojt ě ch HOLUB

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  1. Breaking HUGO with four features jessicaFRIDRICH janKODOVSKÝ miroslavGOLJAN vojtěchHOLUB

  2. HUGO “preserves” the joint distribution of differences among four neighboring pixels. Thus, looking at the histogram of absolute differences between neighboring pixels makes no sense. There is nothing more deceiving than an obvious fact. Sherlock Holmes and GökhanGül

  3. Why bother looking at the histogram? Form the histogram of absolute differences of all horizontally, vertically, diagonally, and minor-diagonally adjacent pixels. Average over the entire BOSSbase: cover +stego

  4. Why around 90? HUGO’s distortion measure takes into consideration only differences up to ±90 Changing pixels with higher differences costs much less (if anything). 90 changes to 91 rather than 89 91 changes to 92 rather than 90

  5. Two dimensional feature vector (h90, h91) Images with more populated bin h90 are better detected. Such images are textured, noisy, and with many edges!

  6. Accuracy on BOSSbase Feature vector: r dim 0.1 0.2 0.3 0.4 0.5 bpp 0 4 67.41 69.06 70.19 70.31 71.41 1 6 67.54 69.65 70.52 70.65 71.78 2 8 67.72 70.01 71.21 71.35 72.39 3 10 68.00 70.26 71.32 71.80 72.46 4 12 68.26 70.59 71.58 71.79 72.78 5 14 68.41 70.57 71.59 71.80 72.75 6 16 68.59 70.60 71.71 71.95 72.91 7 18 68.74 70.55 71.75 71.79 72.93 8 20 68.76 70.59 71.81 71.90 73.03 9 22 68.78 70.60 71.79 71.85 73.13 10 24 68.77 70.54 71.82 71.85 73.10 Even though bins outside of [89,92] are preserved, including them helps as they serve as a reference. Note: nearly constant performance w.r.t. payload!

  7. This thing detects textured images! MINMAX (dim 1458) cooc bins thresholded with low threshold T FLD scatter plots Four histogram features Complementary performance!

  8. Steganalysts join forces Performance on BOSSbase over 100 splits 8074/1000 (trn/tst): Behemoth (24,933): 84.22% + four histogram features: 70.31% Together (24,937 ensemble): 90.01% CDF (G-SVM) 72.70% + four histogram features: 70.31% Together (1,238 G-SVM): 79.43%

  9. What if HUGO had T= 255 instead of 90? Just run hugo_simulator.exe --T As expected, the histogram features become completely ineffective.

  10. Our current best results on HUGO on BOSSbase BOSS setting with payload 0.4 bpp, T = 90 (average over 100 splits 8074/1000) dim accuracy+4 325 78.80 84.78 663 81.55 87.84 1313 83.23 -- 1638 84.12 -- 4888 85.82 91.02 11,310 86.38 -- 33,930 87.15 92.02 Note that this 1638-dim feature has the same performance as our 33,963-dim behemoth (our best result in January).

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