GhostBusters | Ensemble model of experts for detecting fake | Machine Learning library
kandi X-RAY | GhostBusters Summary
kandi X-RAY | GhostBusters Summary
GhostBusters is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. GhostBusters has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However GhostBusters build file is not available. You can download it from GitHub.
In this repository you will find a Python Keras implementation of GhostBusters, including download links to the pre-trained models and datasets. The model is based on the following publication:. GhostBusters is a proposed countermeasure against Phantom attacks on driverless vehicles and advanced driver assist systems (ADAS). A Phantom attack is where an object is projected or digitally displayed for a split-second near a vehicle causing the vehicle to behave unpredictably. For example, the projection of a person on the road can trigger the collision avoidance system, causing the car to stop or swerve in a dangerous manner. Another example is where a false road sign is projected on a wall nearby which alters the car's speed limit. This attack raises great concern, because unskilled attackers can use split-second phantom attacks against ADASs with little fear of getting caught, because. To counter this threat, we propose GhostBusters: a committee of machine learning models which validates objects detected by the on-board object detector. The GhostBusters can be deployed on existing ADASs without the need for additional sensors and does not require any changes to be made to existing road infrastructure. It consists of four lightweight deep CNNs which assess the realism and authenticity of an object by examining the object's reflected light, context, surface, and depth. A fifth model uses the four models' embeddings to identify phantom objects. Through an ablation study we have found that by separating the aspects our solution is less reliant on specific features. This makes it more resilient than the baseline model and robust against adversarial attacks. For example, it is hard to make a physical adversarial sample on optical flow.
In this repository you will find a Python Keras implementation of GhostBusters, including download links to the pre-trained models and datasets. The model is based on the following publication:. GhostBusters is a proposed countermeasure against Phantom attacks on driverless vehicles and advanced driver assist systems (ADAS). A Phantom attack is where an object is projected or digitally displayed for a split-second near a vehicle causing the vehicle to behave unpredictably. For example, the projection of a person on the road can trigger the collision avoidance system, causing the car to stop or swerve in a dangerous manner. Another example is where a false road sign is projected on a wall nearby which alters the car's speed limit. This attack raises great concern, because unskilled attackers can use split-second phantom attacks against ADASs with little fear of getting caught, because. To counter this threat, we propose GhostBusters: a committee of machine learning models which validates objects detected by the on-board object detector. The GhostBusters can be deployed on existing ADASs without the need for additional sensors and does not require any changes to be made to existing road infrastructure. It consists of four lightweight deep CNNs which assess the realism and authenticity of an object by examining the object's reflected light, context, surface, and depth. A fifth model uses the four models' embeddings to identify phantom objects. Through an ablation study we have found that by separating the aspects our solution is less reliant on specific features. This makes it more resilient than the baseline model and robust against adversarial attacks. For example, it is hard to make a physical adversarial sample on optical flow.
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Quality
Security
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GhostBusters has a low active ecosystem.
It has 16 star(s) with 4 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 1 have been closed. On average issues are closed in 12 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of GhostBusters is current.
Quality
GhostBusters has 0 bugs and 0 code smells.
Security
GhostBusters has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
GhostBusters code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
GhostBusters is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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GhostBusters releases are not available. You will need to build from source code and install.
GhostBusters has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions, examples and code snippets are available.
It has 673 lines of code, 25 functions and 7 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed GhostBusters and discovered the below as its top functions. This is intended to give you an instant insight into GhostBusters implemented functionality, and help decide if they suit your requirements.
- Train the model
- Train the trained models
- Train the experts
- Split the data into training test sets
- Create a CNN
- Extracts data from a video
- Extract features from two images
- Extracts the non - sign from the image
- Crop the given image
- Evaluate the ROC curve
- Predict for a given path
- Get the embeddings for the given generator
- Extract features from an image
- Predict output for a given path
- Compute the ROC curve
Get all kandi verified functions for this library.
GhostBusters Key Features
No Key Features are available at this moment for GhostBusters.
GhostBusters Examples and Code Snippets
No Code Snippets are available at this moment for GhostBusters.
Community Discussions
Trending Discussions on GhostBusters
QUESTION
KnockoutJS: select option with a background image
Asked 2022-Jan-06 at 17:40
My code looks something like this at the moment:
...ANSWER
Answered 2022-Jan-06 at 17:40Perhaps there is a better solution, but you could use the parameter optionsAfterRender in the Options binding in order to modify the tag:
QUESTION
Selecting key/value pairs from API Response using Dictionary Comprehension with If/Else conditions
Asked 2020-Sep-04 at 18:14
I have compiled a list of API response called `user_responses'. This is an example of a response in the list:
...ANSWER
Answered 2020-Sep-04 at 18:14A good way to reduce for-loops into comprehensions, is to first visualize the process using a regular for loop:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install GhostBusters
Tested on an Ubuntu System with 256GB RAM, Xeon E7 CPUs: using one Nvidia Titan RTX (24GB), Driver 450.51.05, CUDA Version 11.0.
Tested using Anaconda 3.8.5, Keras with the tensorflow back-end v2.3, and v2.2 GPU (see environment.yaml)
Download the pre-trained models from here and put them into the 'models' directory.
If you want to train/execute the model on our datasets, then download them from here and put them into the 'data' directory.
Install the dependencies:
For creating a new dataset, you will need access to the TensorFlow Object Detection API, Protobuf, and OpenCV.
Download the pre-trained object detection model (faster_rcnn_inception_resnet_v2_atrous) from here and put it in the models directory (original source).
Install the Tensorflow Object Detection API (complete instructions here): Install Google Protobuf: Run: sudo apt-get install autoconf automake libtool curl make g++ unzip From https://github.com/protocolbuffers/protobuf/releases, download the protobuf-all-[VERSION].tar.gz. Extract the contents and cd into the directory Run: ./configure make make check sudo make install sudo ldconfig # refresh shared library cache. Check if it works: protoc --version Install the Detection API: Download the API to a directory of your choice: git clone https://github.com/tensorflow/models.git cd into the TensorFlow/models/research/ directory Run: python -m pip install . Install OpenCV Run: pip install opencv-python
To train the GhostBusters model, you will need three preprocessed datasets (real signs, fake signs, and nosigns). Videos taken at night so the distribution will fit the phantoms as well. The video should be taken from driver's point of view (front of car) and can be recorded while casually driving around. To extract real signs from a video, run. where <vid_path> is the the path to your video. Under the directory called 'data', two subdirectories will be made: 'real' containing the processed road signs and 'real_nosign' containing examples with no signs for training the context expert. You can change the data directory by using the -dd <data_dir> flag. Similarly, to extract fake signs from a video, run. The processed signs will be saved to 'data/fake/' unless the -dd <data_dir> flag is used, where <data_dir> is the alternate directory path. You can run these commands multiple times on different videos since the data files are saved with unique filenames based on a hash of the input video name.
Tested using Anaconda 3.8.5, Keras with the tensorflow back-end v2.3, and v2.2 GPU (see environment.yaml)
Download the pre-trained models from here and put them into the 'models' directory.
If you want to train/execute the model on our datasets, then download them from here and put them into the 'data' directory.
Install the dependencies:
For creating a new dataset, you will need access to the TensorFlow Object Detection API, Protobuf, and OpenCV.
Download the pre-trained object detection model (faster_rcnn_inception_resnet_v2_atrous) from here and put it in the models directory (original source).
Install the Tensorflow Object Detection API (complete instructions here): Install Google Protobuf: Run: sudo apt-get install autoconf automake libtool curl make g++ unzip From https://github.com/protocolbuffers/protobuf/releases, download the protobuf-all-[VERSION].tar.gz. Extract the contents and cd into the directory Run: ./configure make make check sudo make install sudo ldconfig # refresh shared library cache. Check if it works: protoc --version Install the Detection API: Download the API to a directory of your choice: git clone https://github.com/tensorflow/models.git cd into the TensorFlow/models/research/ directory Run: python -m pip install . Install OpenCV Run: pip install opencv-python
To train the GhostBusters model, you will need three preprocessed datasets (real signs, fake signs, and nosigns). Videos taken at night so the distribution will fit the phantoms as well. The video should be taken from driver's point of view (front of car) and can be recorded while casually driving around. To extract real signs from a video, run. where <vid_path> is the the path to your video. Under the directory called 'data', two subdirectories will be made: 'real' containing the processed road signs and 'real_nosign' containing examples with no signs for training the context expert. You can change the data directory by using the -dd <data_dir> flag. Similarly, to extract fake signs from a video, run. The processed signs will be saved to 'data/fake/' unless the -dd <data_dir> flag is used, where <data_dir> is the alternate directory path. You can run these commands multiple times on different videos since the data files are saved with unique filenames based on a hash of the input video name.
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