rembg VS CyberChef

A bit conflicted as the linked video is also linked from the actual rembg repo but it seems way faster and more detailed to just read the readme at that repo first, and maybe use a video if something doesn’t make sense.

There are open source tools like rembg (https://github.com/danielgatis/rembg) which call into pre-trained models.

You can use Rembg extension to remove background automatically: https://github.com/danielgatis/rembg

Hi guys 📷 I am new to ml deployment. Can anyone help about production deployment ? I made fastapi docker app which removes background from image (https://github.com/danielgatis/rembg). It uses u2net segmentation. I tried aws ec2, lambda, googld cloudrun and so far ec2(t2-large) is the fastest but still too slow. Also, it costs way more than I expected. Are there any other solution that I can deploy ml app with as low as possible? Where do you guys mostly deploy ml app?

I am currently using rembg https://github.com/danielgatis/rembg

rembg

A python script with rembg could work

4.) rembg -- backgrounds be gone. If there isn't an extension in Automatic1111 for this yet, there should be (I haven't checked recently). Same deal as midas, you can point it at a folder and zap the backgrounds off of all your images. Useful in combo with midas and imagemagick if, for instance, you want images of an object on a white background (Stable Diffusion training via LoRAs/Dreambooth may not benefit, but other things like GANs prefer that sort of training image). Useful if you want to "compose" a scene and you have images of dispirate objects/people you want in that scene.

Ran into another issue with " LoadLibrary failed with error 126 Here's the solution: https://github.com/danielgatis/rembg/issues/312

Then we take the encrypted text and use CyberChef to decrypt it.

Let's go to CyberChef and insert our pieces of evidence.

A parameter was changed from '2AMBCgIQBg' to 'CgIIAdgDAQ%3D%3D' which is just the correct base64 encoding they should have been using the entire time.

I don't think this was a hostile action by Google, I think someone just added better input validation for security reasons and it accidently broke the bad requests they were sending.

https://gchq.github.io/CyberChef/#recipe=URL_Decode()From_Ba...

❗This is indeed the flag, but the text is encrypted with Base64. Usually, the presence of padding character "=" indicates that's Base64 type of encoding (but that's only one of the hints). To decrypt it, we can use CyberChef. Copy-paste the text and we either:

It uses a combination of magic bytes (like the `file` command), entropy analysis and character frequency detection to determine whether an output is likely to be of interest to the user.

The file type mechanism is written here[0]. There's a list of all signatures we detect here[1].

[0] https://github.com/gchq/CyberChef/blob/master/src/core/lib/F...

I think this can be deciphered using CyberChef...