A question about robustness

[ybL-123]

Hello, I am interested in your project. I would like to ask if there is any related research on the robustness of this project, and if it has a certain degree of robustness. Thank you!

[Asirwad]

I didn't fully understand your query, could you please elaborate

[ybL-123]

Thank you very much for your reply.
I used a translation tool, so it might not be entirely accurate.
I would like to ask if your project has a certain level of robustness. Can encrypted images be successfully decrypted after being attacked? Additionally, I encountered some issues when running the project:
When I use image hide, it fails to run and shows "failed to hide the image".
When I use super resolution and generate image, the program encounters a 0xC0000409 stack overflow.
“2024-07-10 21:41:59.672038: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.”
thank you for your reply！

[Asirwad]

Image hiding using the model is not very robust since the steg image still has some artifacts of the secret image, that's why I used another layer of encryption using AES or Blowfish cryptographic algorithms

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On Wed, Jul 10, 2024, 7:23 PM ybL-123 ***@***.***> wrote: Thank you very much for your reply. I used a translation tool, so it might not be entirely accurate. I would like to ask if your project has a certain level of robustness. Can encrypted images be successfully decrypted after being attacked? Additionally, I encountered some issues when running the project: When I use image hide, it fails to run and shows "failed to hide the image". When I use super resolution and generate image, the program encounters a 0xC0000409 stack overflow. “2024-07-10 21:41:59.672038: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.” thank you for your reply！ — Reply to this email directly, view it on GitHub <#2 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AUNCURHE4G7VVBU3TXECD4TZLU4D3AVCNFSM6AAAAABKUWSMMWVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEMRQGU3DOOBTGA> . You are receiving this because you commented.Message ID: ***@***.***>

[Asirwad]

This is the version of tensorflow , please cross check with your system