Our application allows users create a database of known videos and query videos against the database to detect pirated content. This is done through a command line interface implemented in Python. All processing is done in C++.
- First install vagrant. Then from the host machine use vagrant to provision a VM.
- To start the VM with virtualbox default provider run
vagrant upwithin the project directory. This will run provisioning if provisioning has not been started. To restart the vm usevagrant reloadwhich will skip provisioning. To connect to the vm, either usevagrant sshor open up virtualbox/VMWare/hyperv and connect graphically. The project folder stays synced to the host machine under/vagrantin the ubuntu client OS. We will add an option to run our programs without debug GUI in the future.
Alternatively, run provisionVM.sh in Ubuntu to install dependencies and build opencv with SIFT
Alternatively, build opencv itself with opencv_contrib. My build string:
cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local \
-DOPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules \
-D WITH_FFMPEG=ON -DOPENCV_ENABLE_NONFREE=ON \
-D BUILD_EXAMPLES=OFF -D WITH_GTK=ON -D BUILD_opencv_apps=OFF -D BUILD_DOCS=OFF \
-D BUILD_PERF_TESTS=OFF -D BUILD_TESTS=OFF <opencv_src>
mkdir build
cd build
cmake ..
make
mkdir build
cd build
cmake .. -DBUILD_TESTING=ON
make
make test
pip install -r requirements.txt
After building the project, run the command line interface from the root project
folder by executing piracy.py
usage: piracy.py [-h] {ADD,QUERY,INFO} ...
Check videos for pirated content. Use ADD to create or update a database. Use
QUERY to check an existing database for one or more videos. Use INFO to see
parameters for an existing database. For more information, try ADD, QUERY, or
INFO with the -h option.
optional arguments:
-h, --help show this help message and exit
type:
{ADD,QUERY,INFO}
Add video(s) to the database or recalculate the database frame/scene vocabulary. When adding multiple videos with optional arguments, frame/scene vocabulary will only be recalculated after the last video is added to save time.
usage: piracy.py ADD [-h] [-kFrame KF] [-kScene KS] [-thresholdScene TS]
dbPath [paths [paths ...]]
positional arguments:
dbPath path to database of known videos
paths path(s) to directories/files to add
optional arguments:
-h, --help show this help message and exit
-kFrame KF k value for frame kmeans
-kScene KS k value for scene kmeans
-thresholdScene TS threshold for inter-scene similarity
kFrame and kScene should be roughly based on the size of your database.
Query the database for each video at paths to check for piracy.
usage: piracy.py QUERY [-h] [-v] [-shortestmatch SM] [--frames] [--subimage]
dbPath paths [paths ...]
positional arguments:
dbPath path to database of known videos
paths path(s) to directories/files to add
optional arguments:
-h, --help show this help message and exit
-v, --visualize visualize video matches
-shortestmatch SM minimum length of matching video clip (in seconds)
--frames match frames instead of scenes; slower but more accurate
--subimage additionally looks for a subimage that shares exactly one
corner (such as a picture-in-picture attack)
If using the -v argument, you will be asked for a path to the directory
containing the video files used to construct the database. You will be able
to select and view matching alignments.
Using the --frames option will be significantly slower. It isn't recommended
for normal use.
If you wish to exclude video clip matches that are too short, specify the
-shortestmatch with minimum number of seconds.
For example, if you only want matches that are more than 3 seconds, you would
type -shortestmatch 3 and you will not see matches that are 3 seconds or less.
Some videos may use a subimage of pirated content, such as in a picture-in-picture attack, so we provide an option to check for a subimage. Note that the subimage must share a corner with the video.
Using the --subimage option will first check for a subimage. If one is found,
we actually run two queries: first with the subimage (called boxvideo.mp4) and
second with the rest of the video with a black box on top of the subimage
(this one is called outervideo.mp4). These videos are stored in your results
folder if you are interested in viewing them. If no subimage is found, the query
proceeds with the entire video.
You can also check for a subimage independently of a query and view the
resulting videos in your results folder if a subimage was found, as the
subimage code is in a separate module in python/subimage_detector.py.
usage: subimage_detector.py [-h] srcpath
Find subimage and save separate files
positional arguments:
srcpath path to video with potential subimage
optional arguments:
-h, --help show this help message and exit
Get info about the database
usage: piracy.py INFO [-h] dbPath
positional arguments:
dbPath path to database of known videos
optional arguments:
-h, --help show this help message and exit
The info will include the kFrame, kScene, and thresholdScene values for
the database as well as a list of videos in the database.
If you have already run a query and would like to view the results again, find
the corresponding logfile in your results folder and run viewer.py
usage: viewer.py [-h] [-v] [-shortestmatch] logfile querypath
View results of query
positional arguments:
logfile path to result logfile
querypath path to query video
optional arguments:
-h, --help show this help message and exit
-v, --visualize visualize video matches
-shortestmatch minimum length of matching video clip (in seconds)
Similarly to a piracy.py QUERY, the -v argument lets you view the matching
clips side by side if you know the path to the videos in the database. If you
wish to exclude video clip matches that are short, specify the
-shortestmatch with a minimum number of seconds.
Create a database from videos in directory /data/videos/ and compute frame/scene
descriptors and scenes:
./piracy.py ADD ./build/database/ ./data/videos/ -kFrame 20000 -kScene 4000 -thresholdScene 30
Check to see if video /data/pirated.mp4 matches any videos in the database:
./piracy.py QUERY ./build/database/ ./data/pirated.mp4
Later, if you want to view the matches again:
./viewer.py ./results/pirated.mp4.csv ./data/pirated.mp4
./visualize <num_points>
This command will save the classified points to visualize.mat and the vocab to vocab.mat
then run gnuplot
gnuplot> set xrange[-100:100]
gnuplot> set yrange[-100:100]
gnuplot> plot 'visualize.mat' with points palette pt 7
If you would like to evaluate the success of the piracy detector, you may use
our script tester.py in the python folder.
Note that you should run tester.py from the root project directory.
$ ./python/tester.py -h
usage: tester.py [-h] [--frames] [--subimage] [-shortestmatch SM]
SOURCEDIR DBPATH
Test attack videos with premade database
positional arguments:
SOURCEDIR path to directory of attack videos
DBPATH path to directory to output testing videos
optional arguments:
-h, --help show this help message and exit
--frames match frames instead of scenes; slower but more accurate
--subimage additionally looks for a subimage that shares exactly one
corner (such as a picture-in-picture attack)
-shortestmatch SM minimum length of matching video clip (in seconds)
The script will tag each video as a "success" or "failure" which is used to create a report. To take advantage of this functionality, you must label your attack videos you test.
If the video is pirated, its name should be "<db name>_<kind of attack>.mp4"
or an extension of your choice, where <db name> is the base name of a video in
the database (without the extension) and <kind of attack> is any string that
describes the attack that you want to appear in the report. Multiple videos with
the same <kind of attack> will appear in the same row of the report.
The only restriction is that <db name> cannot contain underscores. Note that
this means the names of the videos in the database also cannot contain
underscores if you would like to use tester.py.
We append _inserted at the end of <kind of attack> if the pirated clip is
inserted into another video that isn't in the database. We use this to test the
ability of our algorithm to detect random small clips. If you do this, your
report will have an extra column for inserted clips.
You may use any naming scheme you would like for videos that don't appear in the database, as the measure of success is whether any match is found or not. To be properly counted, the video name up until the first underscore (or the whole video name if there is no underscore) cannot appear anywhere within any of the names of the videos in the database.
Let's say your database has the following folders:
video.mp4
example.mp4
sample.mp4
The following are acceptable labels for videos that pirate these:
video_exact_match.mp4
example_projection.mp4
sample_frame_rate_up.mp4
The following are acceptable labels for videos that are not pirated:
not_a_pirated_video.mp4
original_video.mp4
The following would not be an acceptable label for an original video:
vid_notpirated.mp4 "vid" appears in video.mp4
After you run tester.py, you can see the report by running:
$ cd python/ ; python3 make_report.py
make_report.py can also be run with a single argument specifying a path to
a pickle containing results generated by tester.py. tester.py automatically
saves this file as results.pkl in the results folder.
If you do not have attack videos in mind to test, you can use our script
pirater.py to make attack videos.
usage: pirater.py [-h] SOURCEDIR DESTDIR EXTRAVID
Generate testing videos with inserter clips.
positional arguments:
SOURCEDIR path to directory of full videos to use as a base
DESTDIR path to directory to output testing videos
EXTRAVID path to extra video not in the db for insertion
optional arguments:
-h, --help show this help message and exit
There are currently 17 types of attacks, and a short clip of each attack is also
inserted into a video that isn't in the database, for a total of 34 videos for
every video in SOURCEDIR.
Feel free to fork and modify the script for your own purposes. You can easily comment out attacks or add attacks. You can also comment out the inserted clip part if you do not want them.
Team:
- Kaelan Mikowicz: @klanmiko
- Chandni Nagda: @cnagda
- Emma Kristovich: @emkristo
- Andrei Blebea: @The-Boogeyman
Thank you to our mentors Brian, Lucas, and Troy at Telestream, Inc.
Our project is under the MIT License. See LICENSE.txt for the full license.