onionstack - A Pictorial Book of Tor Hidden Services.

├── .gitignore ├── LICENSE ├── README.md ├── active.py ├── active.txt ├── cbir.py ├── clustering.py ├── config.txt ├── docs ├── css │ ├── bootstrap.min.css │ └── viewer.min.css ├── images │ ├── loading.svg │ └── www.torproject.png ├── index.html └── js │ ├── imagesloaded.min.js │ ├── jquery.lazyload.min.js │ ├── masonry.pkgd.min.js │ └── viewer.min.js ├── format.sh ├── images ├── 404.png ├── blank.png ├── blank2.png └── onionstack.png ├── item.py ├── list.txt ├── onionstack.py └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | *.png 2 | ghostdriver.log 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 ntddk 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # onionstack 2 | onionstack is a project to create a pictorial book of Tor hidden services.
3 | You can see our sample page at [ntddk.github.io/onionstack](https://ntddk.github.io/onionstack/). 4 | 5 | ![onionstack](images/onionstack.png) 6 | 7 | ## Requirements 8 | * Python 2.7.* 9 | * Node.js 10 | 11 | ## Installation 12 | ``` 13 | sudo apt-get install tor 14 | /etc/init.d tor restart 15 | npm install -g phantomjs 16 | pip install -r requirements.txt 17 | python onionstack.py -c config.txt -l list.txt 18 | ``` 19 | 20 | ## Contents 21 | * `format.sh` 22 | - Outputs `list.txt` from given files. 23 | * `list.txt` 24 | - The list of targets to crawl. 25 | * `onionstack.py` 26 | - The crawler. 27 | * `active.py` 28 | - Outputs `active.txt` from crawling results. 29 | * `active.txt` 30 | - The list of active onion domains. 31 | * `cbir.py` 32 | - Content-based image retrieval script. 33 | * `clustering.py` 34 | - k-means++. 35 | * `item.py` 36 | - Outputs html snippets for sample page. 37 | 38 | ## Disclaimer 39 | This repository is for research purposes only.
40 | We do NOT in any way promote or encourage any criminal or illegal activity. 41 | 42 | -------------------------------------------------------------------------------- /active.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | #coding: utf-8 3 | 4 | import sys 5 | import os 6 | import hashlib 7 | 8 | def md5(filename): 9 | with open(filename, 'rb') as f: 10 | data = f.read() 11 | return hashlib.md5(data).hexdigest() 12 | 13 | filenames = os.listdir(sys.argv[1]) 14 | for filename in sorted(filenames): 15 | if md5(filename) != md5('blank.png') and md5(filename) != md5('blank2.png') and md5(filename) != md5('404.png') : 16 | print filename[0:16] 17 | 18 | -------------------------------------------------------------------------------- /active.txt: -------------------------------------------------------------------------------- 1 | 222222222g4bgdec 2 | 22222222m4cetz5t 3 | 2222222afubjlbhm 4 | 222222c6hrrxbydu 5 | 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z2gu35md7bi3ceew 1747 | z2hjm7uhwisw5jm5 1748 | z2huz7tsxluvnxoc 1749 | z2kbkvb573a6devu 1750 | z2luqg4xu5r6zm6o 1751 | z2wyy3l6luhctsoa 1752 | z373bxyt6zhmxepx 1753 | z4kzldluie37m5vp 1754 | z5hns3zhhne7z6bl 1755 | z7zbg7sxhxfergpo 1756 | zaebokkun336dmmd 1757 | zakectglg6r3caz4 1758 | zanzibbk7qjqo7oj 1759 | zanzibe4e4p4jdh2 1760 | zdasgqu3geo7i7yj 1761 | zdfvqospmrbvzdn3 1762 | ze43ghupq4i44yzh 1763 | zerobinqmdqd236y 1764 | zgfgvob256pffy62 1765 | zgrl6sghf5jh37zz 1766 | zhtmkxfe7liw3adu 1767 | ziadoxvr77y7ifvs 1768 | zirpcb4q7edrw5e2 1769 | zj45fq6q5f7m56z2 1770 | zkkc7e5rwvs4bpxm 1771 | zktxwox376pbkp2z 1772 | zlal32teyptf4tvi 1773 | zmovietoropzaid3 1774 | zmxzzvcvm6oqvckg 1775 | zn4ei5n3xddb3otv 1776 | zndclh5h5zr6fgp3 1777 | znig4bc5rlwyj4mz 1778 | znsxb5weeh44pxwk 1779 | zohaq6hhk2p52c7d 1780 | zoo6cxl4rtac3jxw 1781 | zootopiaakx4m57x 1782 | zpvluacf3b3cjxm7 1783 | zqktlwi4fecvo6ri 1784 | zqsod2vcfc7v66ec 1785 | zrwxcayqc4jgggnm 1786 | zrxdzzwlxd35tbf5 1787 | zsjcgkysxbds65as 1788 | zsolxunfmbfuq7wf 1789 | zt6bycgnjvatzzvi 1790 | zuag53c4qgqiw2qr 1791 | zucj3lagcb4w6yap 1792 | zupyv3e5spdok6nw 1793 | zuqxbsc6sty2tedj 1794 | zwknk7ze3jojg7l5 1795 | zxjfjm5iinmgezyj 1796 | zy7fh3quj2rmloun 1797 | zzzcgjit65yyn4ji 1798 | -------------------------------------------------------------------------------- /cbir.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | #coding: utf-8 3 | 4 | import sys 5 | import glob 6 | import cv2 7 | 8 | list = glob.glob('./images/*.png') 9 | 10 | def main(): 11 | target_im = cv2.imread(sys.argv[1]) 12 | target_hist = cv2.calcHist([target_im], [0], None, [256], [0, 256]) 13 | 14 | for i in list: 15 | comparing_im = cv2.imread(i) 16 | comparing_hist = cv2.calcHist([comparing_im], [0], None, [256], [0, 256]) 17 | diff = cv2.compareHist(target_hist, comparing_hist, 0) 18 | if diff > float(sys.argv[2]): 19 | print i, 20 | print diff 21 | 22 | if __name__ == '__main__': 23 | main() 24 | -------------------------------------------------------------------------------- /clustering.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | #coding: utf-8 3 | 4 | import sys 5 | import glob 6 | import cv2 7 | import numpy as np 8 | from sklearn.decomposition import TruncatedSVD 9 | from sklearn.preprocessing import Normalizer 10 | from sklearn.cluster import KMeans 11 | import os 12 | import shutil 13 | 14 | n_clusters = int(sys.argv[1]) 15 | list = glob.glob('./images/*.png') 16 | 17 | def main(): 18 | features = [] 19 | 20 | for i in list: 21 | im = cv2.imread(i) 22 | hist, bins = np.histogram(im.ravel(), 256, [0, 256]) 23 | features.append(hist) 24 | 25 | lsa = TruncatedSVD(10) 26 | features = lsa.fit_transform(features) 27 | features = Normalizer(copy = False).fit_transform(features) 28 | 29 | km = KMeans( 30 | init='k-means++', 31 | n_clusters=n_clusters, 32 | ) 33 | km.fit(features) 34 | 35 | for i in range(n_clusters): 36 | if not os.path.exists('./result/' + str(i)): 37 | os.makedirs('./result/' + str(i)) 38 | 39 | cnt = 0 40 | 41 | for i in list: 42 | filename = i.split('/')[-1] 43 | print filename, 44 | print km.labels_[cnt] 45 | shutil.copyfile(i, './result/' + str(km.labels_[cnt]) + '/' + filename) 46 | cnt += 1 47 | 48 | if __name__ == '__main__': 49 | main() 50 | 51 | -------------------------------------------------------------------------------- /config.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ntddk/onionstack/e52cf9ba4afa42fb459a31ff28ac231aed5e8e4c/config.txt -------------------------------------------------------------------------------- /docs/css/viewer.min.css: -------------------------------------------------------------------------------- 1 | /*! 2 | * Viewer.js v0.7.1 3 | * https://github.com/fengyuanchen/viewerjs 4 | * 5 | * Copyright (c) 2017 Fengyuan Chen 6 | * Released under the MIT 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