Image-Forgery-Detection-CNN
Image forgery detection using convolutional neural networks. Group 10's final project for TU Delft's course CS4180 Deep Learning 2019. (by kPsarakis)
| Image-Forgery-Detection-CNN | Video_Forgery_Detection_Using_Machine_Learning | |
|---|---|---|
| 2 | 2 | |
| 137 | 4 | |
| - | - | |
| 0.0 | 10.0 | |
| 10 months ago | about 4 years ago | |
| Python | Jupyter Notebook | |
| - | - |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Image-Forgery-Detection-CNN
Posts with mentions or reviews of Image-Forgery-Detection-CNN.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-03.
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Question to the Mods: What is the exact criteria for removing new sighting submissions?
So far the bot archives only on the Telegram chann image/video posts that are posted to this subreddit exclusively hosted by the reddit uploaded media servers. It only tries to classify still images, using a small pre-trained dataset ueint from this GitHub repo: https://github.com/kPsarakis/Image-Forgery-Detection-CNN
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Update on the UAP.observer project that aims to instantly archive & classify forgery vs authentic footage.
Today I continued working on the script from yesterday utilizing the help of this GitHub repository (shout out to these guys!): https://github.com/kPsarakis/Image-Forgery-Detection-CNN I managed to get some results already.
Video_Forgery_Detection_Using_Machine_Learning
Posts with mentions or reviews of Video_Forgery_Detection_Using_Machine_Learning.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-03.
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Experiment: A.I. Machine Learning Algorithm Classifies tha Bolivian Tic-Tac video as authentic (no digital tampering has been detected in any frame).
So most of you probably already saw the Tic-Tac shaped UFO that seems to zap in Bolivia. So I decided to do a little experiment to rule out the possibility of CGI at least in this scenario. I picked this Video Forgery Detector Module: https://github.com/ShobhitBansal/Video_Forgery_Detection_Using_Machine_Learning
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Question to the Mods: What is the exact criteria for removing new sighting submissions?
Video's are currently not being clasified but I am experimenting currentlt with this model's pre-trained weighted file also: https://github.com/ShobhitBansal/Video_Forgery_Detection_Using_Machine_Learning
What are some alternatives?
When comparing Image-Forgery-Detection-CNN and Video_Forgery_Detection_Using_Machine_Learning you can also consider the following projects:
awesome-colab-notebooks - Collection of google colaboratory notebooks for fast and easy experiments
DOLG-pytorch - Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
SincNet - SincNet is a neural architecture for efficiently processing raw audio samples.
classification - Classification of the MNIST dataset using various Deep Learning techniques