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[NeurIPS 2023] Official PyTorch implementation for the paper "CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography"

Python 100.00%

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cross's Issues

您好,我打算在我的小论文中引用您的论文

您好,我是来自北京邮电大学的一名博士生,看来您的论文,我非常受启发,感觉您提出了一个隐写术的新方向,原始的隐写术要求嵌入信息要小于载体,您这种无载体的隐写术可以使得嵌入信息与容器图像大小相同。
我有个问题,您给的requirements.txt中的库我都下不到,我试了好几个镜像都不行,请您给我一个镜像或下载地址好吗,感激不尽。

Stego image question

`

latent_noise = ode.invert(prompt_1, image_gt_latent, is_forward=True)
image_hide_latent = ode.invert(prompt_2, latent_noise, is_forward=False)

# save container image
image_hide = ode.latent2image(image_hide_latent)
cv2.imwrite("{:s}/hide.png".format(args.save_path), cv2.cvtColor(image_hide, cv2.COLOR_RGB2BGR))

# reveal process
latent_noise = ode.invert(rev_prompt_2, image_hide_latent, is_forward=True)

image_reverse_latent = ode.invert(rev_prompt_1, latent_noise, is_forward=False)
image_reverse = ode.latent2image(image_reverse_latent)`

`

In this piece of code, it seems like you are directly using the latent vector (1*4*64*64) that has not gone through vae.decoder for secret message extraction. In the concept of image steganography, we should use the image generated after vae.decoder (e.g. 1*3*512*512) as the secret message container.

As in the following code:

`

latent_noise = ode.invert(prompt_1, image_gt_latent, is_forward=True)
image_hide_latent = ode.invert(prompt_2, latent_noise, is_forward=False)

# save container image
image_hide = ode.latent2image(image_hide_latent)
cv2.imwrite("{:s}/hide.png".format(args.save_path), cv2.cvtColor(image_hide, cv2.COLOR_RGB2BGR))

hide_latent_for_reveal = ode.image2latent(image_hide)

# reveal process
latent_noise = ode.invert(rev_prompt_2, hide_latent_for_reveal, is_forward=True)

image_reverse_latent = ode.invert(rev_prompt_1, latent_noise, is_forward=False)
image_reverse = ode.latent2image(image_reverse_latent)`

`

About the Stego260

I have read your paper and I am curious about the dataset Stego260.I want to use your model using different methods to hide two images,but I don't see any access to the dataset.Could you please offer it?Thanks a lot.

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