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基于 WGAN 的信息隐藏 PowerPoint Presentation
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基于 WGAN 的信息隐藏

基于 WGAN 的信息隐藏

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基于 WGAN 的信息隐藏

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  1. 基于WGAN的信息隐藏 • Information hiding based on Wasserstein Generator Adversarial Networks Speaker: tangmingrui

  2. Examples of information hiding

  3. Examples of information hiding

  4. Examples of information hiding hidden cover hidden cover hidden (noisy) reveal secret reveal

  5. Information hiding • Algorithms LSB DCT steganography

  6. LSB(Least Significant Bit) algorithm • Image pixels are generally composed of RGB three primary colors (red, green, and blue), each of which occupies 8 bits. • There are 2563 colors in RGB model, and the human eye can distinguish only a small part, which leads to the fact that when we modify the lowest binary bit of the RGB color component, our naked eyes can't distinguish it. • (0x00~0xFF)

  7. LSB embedding algorithm • Convert the spatial domain pixel value of the original carrier image from decimal to binary representation 255 253 254 253 255 253 252 255 254 11111111 11111101 11111110 11111101 11111111 11111101 11111100 11111111 11111110

  8. LSB embedding algorithm • Replacing the least significant bit of the corresponding carrier data with each bit information in the binary secret information 11111111 11111101 11111110 11111101 11111111 11111101 11111100 11111111 11111110 11111110 11111101 11111111 11111100 11111110 11111100 11111101 11111110 11111110

  9. LSB embedding algorithm • Converting the obtained binary data containing secret information into decimal pixel values, thereby obtaining an image with secret information 11111110 11111101 11111111 11111100 11111110 11111100 11111101 1111111011111110 254 253 255 252 254 252 253 254 254

  10. LSB extraction algorithm • Converting the obtained carrier image with hidden information into decimal data values into binary data 11111110 11111101 11111111 11111100 11111110 11111100 11111101 11111110 11111110 254 253 255 252 254 252 253 254 254

  11. LSB extraction algorithm • The least significant bit of the binary data is extracted, which is the secret information sequence. 11111110 11111101 11111111 11111100 1111111011111100 1111110111111110 11111110 0 1 1 0 0 0 1 0 0

  12. Example

  13. Advantages and disadvantages • Advantages: • The algorithm is fast in calculation ,can be improved quickly • The capacity is large enough(1/8-1/4 of the total) • Disadvantages: • Poor robustness 、Weak JPEG compression • Can only handle simple stream format files

  14. DCT steganography • The sender divides the carrier image into 8 × 8 sub-blocks, and adjusts the relative size of the two DCT IF coefficients in each sub-block to hide a secret information bit.

  15. DCT steganography • Use (u1,v1),(u2,v2) to represent these two indexes • Bi represent the I DCT transformed image block • Encode:If the block Bi (u1, v1) > Bi(u2, v2) , coded as "1", otherwise the code is "0". If the relative size does not match the bit to be encoded, exchange two coefficients with each other. • By adding a random value to the two coefficients to ensure that for a certain x>0, •    Let |Bi (u1,v1)-Bi(u2,v2) |>x

  16. Advantages and disadvantages • Advantage: • Strong robustness • Could be widely used(jpeg) • Disadvantage: • Large amount of computation • Capacity is not large enough

  17. WGAN(Wasserstein Generator Adversarial Networks) • Definition of GAN(minimax game) D(x): Probability of judging whether a real picture is true

  18. WGAN • The last layer of the discriminator removes sigmoid • Generator and discriminator loss does not take log • Each time the parameters of the discriminator are updated, their absolute values are truncated to no more than a fixed constant c • Do not use momentum-based optimization algorithms (including momentum and Adam), recommend RMSProp , SGD also

  19. WGAN • G: Driven by noise, an image of approximate real data samples is generated by machine learning as carrier information. • D: Using the secret information (steg( G( z) ) ) and the real sample as the input of the discriminant model D, the authenticity of the input sample is authenticated • S: Using complex GNCNN (Gussian-Neuron Convolutional Neural Network) to evaluate the suitability of the generated image while determining whether the image to be inspected has a steganographic operation..

  20. WGAN • Model network architecture

  21. Examples

  22. Reference • https://blog.51cto.com/bluejay/873313 • https://zhuanlan.zhihu.com/p/24767059 • https://zhuanlan.zhihu.com/p/25071913 • https://wenku.baidu.com/view/bb85a68671fe910ef12df8f4.html • 王耀杰, 钮可, & 杨晓元. (2018). 基于生成对抗网络的信息隐藏方案. 计算机应用,38(10), 177-182.

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