![logo](images/logo.png)
cloacked-pixel
==========
Platform independent Python tool to implement LSB image steganography and a basic detection technique. Features:
- Encrypt data before insertion.
- Embed within LSBs.
- Extract hidden data.
- Basic analysis of images to detect LSB steganography.
How to use:
$ python lsb.py
LSB steganogprahy. Hide files within least significant bits of images.
Usage:
lsb.py hide <img_file> <payload_file> <password>
lsb.py extract <stego_file> <out_file> <password>
lsb.py analyse <stego_file>
Hide
----
All data is encrypted before being embedded into a picture. Encryption is not optional. Two consequences of this are that:
- The payload will be slightly larger.
- The encrypted payload will have a high entropy and will be similar to random data. This is why the frequency of 0s and 1s in the LSB position should be the same – 0.5. In many cases, real images don’t have this propriety and we’ll be able to distinguish unaltered images from the ones with embedded data. More below.
Encrypt and hide an archive:
$ python lsb.py hide samples/orig.jpg samples/secret.zip p@$5w0rD
[*] Input image size: 640x425 pixels.
[*] Usable payload size: 99.61 KB.
[+] Payload size: 74.636 KB
[+] Encrypted payload size: 74.676 KB
[+] samples/secret.zip embedded successfully!
Original image:
![original image](images/orig.jpg)
Image with 75k archive embedded:
![Embedded archive](images/stego.jpg)
Extract
-------
$ python lsb.py extract samples/orig.jpg-stego.png out p@$5w0rD
[+] Image size: 640x425 pixels.
[+] Written extracted data to out.
$ file out
out: Zip archive data, at least v1.0 to extract
Detection
---------
A simple way to detect tampering with least significant bits of images is based on the observation above – regions within tampered images will have the average of LSBs around 0.5, because the LSBs contain encrypted data, which is similar in structure with random data. So in order to analyse an image, we split it into blocks, and for each block calculate the average of LSBs. To analyse a file, we use the following syntax:
$ python lsb.py analyse <stego_file>
**Example**
![Castle](images/castle.jpg)
Now let’s analyse the original:
$ python lsb.py analyse castle.jpg
![Original iamge analysis](images/analysis-orig.png)
… and now the one containing our payload:
$ python lsb.py analyse castle.jpg-stego.png
![Stego image analysis](images/analysis-stego.png)
Notes
-----
- It is entirely possible to have images with the mean of LSBs already very close to 0.5. In this case, this method will produce false positives.
- More elaborate theoretical methods also exist, mostly based on statistics. However, false positives and false negatives cannot be completely eliminated.
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一套misc图片隐写练习题以及配套的所有工具
共42个文件
png:12个
jpg:9个
py:5个
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2024-05-13
21:39:01
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Misc_Picture_Steganography.zip (42个子文件)
Misc_Picture_Steganography
tools
cloacked-pixel
crypt.py 889B
lsb.py 5KB
images
logo.png 154KB
stego.jpg 122KB
analysis-stego.png 75KB
analysis-orig.png 85KB
orig.jpg 115KB
castle.jpg 51KB
README.md 3KB
BlindWaterMark
wm.png 4KB
wm_from_hui.png 161KB
LICENSE 34KB
hui_with_wm.png 318KB
bwm.py 5KB
bwmforpy3.py 7KB
requirements.txt 42B
hui.png 277KB
README.md 2KB
setup.py 1KB
Stegsolve.jar 305KB
winhex.7z 5.32MB
题目
admin.exe 35KB
flag.jpg 34KB
hex.jpg 2.19MB
no_hex 3.67MB
shack.png 10.01MB
dog.jpg 86KB
ocean.jpg 42KB
100_KHf05OI.gif 2.36MB
final 3.67MB
hui_with_wm.png 318KB
glance.gif 349KB
final.png 3.67MB
misc_bmp 2.76MB
FindHideMsg.png 7.18MB
hui.png 277KB
timg_orginal.jpg 169KB
cat.jpg 858KB
install.sh 441B
requirements.txt 44B
.gitignore 2KB
README.md 681B
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