nemozone

a zone for no one and everyone :) Btw this blog is only for adults! Dieser Blog ist nur für Erwachsene!

Hinter Tatort Rechts stehen Johannes Filter und Anna Neifer. Seit Sommer 2020 arbeiten wir an diesem Projekt, haben vieles verworfen und am Ende liest du jetzt diese Zeilen. Mit Tatort Rechts wollen wir einen Schwerpunkt setzen zu rechter, rassistischer und antismetischer Gewalt. Wir wollen offen legen: diese Gewalt passiert überall. Wenn du wissen willst wo, dann nutze unser Recherche-Tool…

Source and Project: https://tatortrechts.de/

Was, wo, wie gebaut wird, bestimmt wie lebenswert eine Stadt auch in der Zukunft sein wird. Deshalb ist es essentiell, dass bereits bei der Planung von Infrastrukturprojekten Informationen über mögliche Klimaänderungen berücksichtigt werden.…

Sources: https://www.zamg.ac.at/cms/de/klima/news/clarity-ein-klima-stadtplanungs-tool-fuer-die-oeffentlichkeit

https://myclimateservices.eu/en/about

https://csis.myclimateservice.eu/

Analyze suspicious files and URLs to detect types of AI-generated visual threats. Beware that your sample submissions must contain human faces, as every analysis will look for signs of manipulation and synthesis on the face area…

Source: https://platform.sensity.ai/deepfake-detection this link isn't free anymore

Another good source:

https://pentestit.com/open-source-deepfake-detection-tool-list/

FALdetector: This open source tool in Python helps you detect Photoshopped faces by helping you script Adobe Photoshop! The basic premise behind this tool is that most malicious photo manipulations are created using standard image editing tools, such as Adobe Photoshop. It does so by detecting image warping edits applied to human faces by implementing a model trained entirely using fake images that were automatically generated by scripting Photoshop itself. This has been theoretically proved by the authors in their academic paper and the open source project. Pretty impressive I must say.

Deepstar: I blogged about this tool about a month ago. Deepstar aka deep* is an open source, AI based toolkit in python that helps you detect deepfake videos. It is also extensible enough to be able to facilitate testing of new detection algorithms. Check out it’s GitHub repository.

Visual DeepFake Detection: This tool takes a different approach for detecting deepfakes. Since different people create different deepfake videos, it is assumed that these videos are created using a variety of deepfake techniques. Furthermore, assumptions about the type of models, their architecture or the type of artifacts they generate are also not made by this tool. Whats more is that the real and fake videos used are completely unrelated! This toolset further augments another dataset called as FaceForensics++. Check out the GitHub repository of this offering by Dessa.

DeepFake Audio Detection: By now, we also know also have techniques and tools to detect deepfaked audio. This deepfake audio detector model is a deep neural network that uses Temporal convolution. First, raw audio is preprocessed and converted into a mel-frequency spectrogram — this is the input for the model. Mel-frequency cepstrum (MFC) is a representation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. The model performs convolutions over the time dimension of the spectrogram, then uses masked pooling to prevent overfitting. Finally, the output is passed into a dense layer and a sigmoid activation function, which ultimately outputs a predicted probability between 0 (fake) and 1 (real). Check out the GitHub repository of this offering by Dessa as well.

Resemblyzer: Resemblyzer allows you to derive a high-level representation of a voice through a deep learning model. Given an audio file of speech, it creates a summary vector of 256 values that summarizes the characteristics of the voice spoken. It helps in fake speech detection by verifying if the speech is legitimate or fake by comparing the similarity of possible fake speech to real speech. Check this project out here.

You can install it as always via the terminal

sudo apt install efibootmgr #Debian based

sudo dnf install efibootmgr #Fedora

sudo zypper install efibootmgr #SUSE/OpenSUSE

Furthermore

# Display current boot order
efibootmgr

# Set the boot order to boot from the USB drive first
efibootmgr -o 0,80

# Add a new boot entry for a Linux distribution
efibootmgr -c -d /dev/sdb -p 2 -L "Linux" -l "\EFI\linux\vmlinuz.efi"

# Remove a boot entry
efibootmgr -b 0001 -B

Note that these examples assume that efibootmgr is installed on the system and that the user has the necessary permissions to run the command. Also, the actual device paths and boot entries may vary depending on the specific system configuration.

# Display verbose information about current boot entries
efibootmgr -v

This command will display detailed information about each boot entry, including the boot order, the device path, the file path, and the label. This can be useful for troubleshooting boot issues or for identifying specific boot entries.

Here is an example of the output you could expect:

BootCurrent: 0002
Timeout: 0 seconds
BootOrder: 0003,0002,0001
Boot0000* Windows Boot Manager  HD(1,GPT,d6a5f6c5-6b4c-4dca-b566-50f9581403e6,0x800,0x32000)/File(\EFI\Microsoft\Boot\bootmgfw.efi)
Boot0001* ubuntu    HD(1,GPT,d6a5f6c5-6b4c-4dca-b566-50f9581403e6,0x800,0x32000)/File(\EFI\ubuntu\shimx64.efi)
Boot0002* Hard Drive    BBS(HD,,0x0)
Boot0003* CD/DVD Drive BBS(CDROM,,0x0)

This output shows the boot current, boot timeout, boot order, and information on each boot entry. BootCurrent shows the entry that is currently being used to boot the system. Timeout is the time in seconds for which the system waits for user input before booting the default entry. BootOrder is the order in which the system looks for boot entries. The first entry that is found will be booted. Each Boot entry shows its name, device, file path, and label.

Please note that the output shown here is just an example and the output will vary based on the system.

To get further info you can use cht.sh as I've showed this tutorial

Source: https://www.linuxbabe.com/command-line/how-to-use-linux-efibootmgr-examples

             
            /\                  /\
            |`\\_,--="=--,_//`|
            \ ."  :'. .':  ". /
             ==)  _ :  '  : _  (==
            |>/O\   _   /O\<|
            | \-"~` _ `~"-/ |
           >|`===. \_/ .===`|<
     .-"-.   \==='  |  '===/   .-"-.
.---{'. '`}---\,  .-'-.  ,/---{.'. '}---.
 )  `"---"`     `~-===-~`     `"---"`  (
  (  I'll eat your face nyan :)         )
  )                                                 (


You can get the output above when you type this into your terimanl after you've installed boxes

echo "I'll eat your face nyan" | boxes -d cat 

Source: https://www.tecmint.com/boxes-draws-ascii-art-boxes-in-linux-terminal/

Open your terminal, then type or copy and paste the command below. Don't forget to save stuff which is not saved

sudo systemctl reboot --firmware-setup

The colors

Open up your terminal install snap asccording to your distro:

And then install this

sudo snap install lolcat

And then taste the colors ;)

Sweet Jebus :D

Source: https://github.com/busyloop/lolcat