Sai Narendra's starred repositories
Leet-Code-Youtube
The purpose of LeetCode is to provide you hands-on training on coding questions. The Online Judge gives you immediate feedback on the correctness and efficiency of your algorithm which facilitates a great learning experience.
linkedin-skill-assessments-quizzes
Full reference of LinkedIn answers 2024 for skill assessments (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) linkedin excel test lösungen, linkedin machine learning test LinkedIn test questions and answers
facefusion
Industry leading face manipulation platform
DeepFake-Image-Detection
Distinguishes between real and fake faces using CNN
deepfake-detection
Identify the images as real or fake using state-of-the-art AI models
deepfake-detection
I present a web based application for automatic detection of replacement and reenactment of deep fakes. I worked on a novel Res-Next convolution neural network for face reenactment which works for pose and expression variations and can be applied to single image or a video sequence.
DeepFakeDetection
Fake image detection using CNN
Deepfake-detection-model-based-on-fake-attributes-shown-in-image-video
[paper] Deepfake detection model based on fake attributes shown in image/video
DeepFake-Detect
Deepfake Image Detection with Keras & TensorFlow
deepfake-image-detection
Predict if an image is real or generated by GANs (fake)
imageForensic
Web Application to analyze image and detect fakeness, object detection and also extract properties and characteristics of the image.
Image-Forgery-Detection
This project classifies the image to either real or fake image by doing two levels of testing. Fake images are the images that are digitally altered.
Deep-Fake-Video-Detection-Using-Deep-Learning-and-Tkinter-GUI
Deep Fake Video/Image has been around since last decade where we are trying to mimic the behaviour, expression, lip and eye movement made by another person on top of another person, which is then synced to each other. These are then used for some illegal and legal activities by people like border crossing, financial thefts etc. So, in order to solve this problem, we can use the model above, where it helps us to identify whether a particular video fake or not using Transfer Learning and LSTM.
DeepFake_Detection_using_Convolutional_Neural_Networks
This project employs artificial intelligence (AI) techniques, specifically machine learning (ML), to address the challenge of detecting DeepFake images. DeepFakes are synthetic media that use deep learning algorithms to manipulate or generate realistic-looking content, often involving human faces.
Image-Forgery-Detection-using-Deep-learning
Image Forgery Detection using Deep learning Graduation project
Deepfake_detection_using_deep_learning
This projects aims in detection of video deepfakes using deep learning techniques like RestNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained RestNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features. For more details follow the documentaion.
Deepfake_detection_using_deep_learning
This projects aims in detection of video deepfakes using deep learning techniques like RestNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained RestNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features. For more details follow the documentaion.
retinaface
RetinaFace: Deep Face Detection Library for Python
DeepFake-Detection-Model
Deepfakes are defined as synthetic media in which a person in an existing image or video is replaced with someone else’s likeness. As time progresses, deepfakes are becoming increasingly sophisticated and in some cases hard to identify for the average person. This will become a problem given that deepfakes may be used maliciously as a source of misinformation, manipulation, harassment, and persuasion. In this project we attempted several different techniques with the sole purpose of identifying deepfake videos.
DEEP-FACE-dectection01
 Read the technical deep dive: https://www.dessa.com/post/deepfake-detection-that-actually-works # Visual DeepFake Detection In our recent [article](https://www.dessa.com/post/deepfake-detection-that-actually-works), we make the following contributions: * We show that the model proposed in current state of the art in video manipulation (FaceForensics++) does not generalize to real-life videos randomly collected from Youtube. * We show the need for the detector to be constantly updated with real-world data, and propose an initial solution in hopes of solving deepfake video detection. Our Pytorch implementation, conducts extensive experiments to demonstrate that the datasets produced by Google and detailed in the FaceForensics++ paper are not sufficient for making neural networks generalize to detect real-life face manipulation techniques. It also provides a current solution for such behavior which relies on adding more data. Our Pytorch model is based on a pre-trained ResNet18 on Imagenet, that we finetune to solve the deepfake detection problem. We also conduct large scale experiments using Dessa's open source scheduler + experiment manger [Atlas](https://github.com/dessa-research/atlas). ## Setup ## Prerequisities To run the code, your system should meet the following requirements: RAM >= 32GB , GPUs >=1 ## Steps 0. Install [nvidia-docker](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)) 00. Install [ffmpeg](https://www.ffmpeg.org/download.html) or `sudo apt install ffmpeg` 1. Git Clone this repository. 2. If you haven't already, install [Atlas](https://github.com/dessa-research/atlas). 3. Once you've installed Atlas, activate your environment if you haven't already, and navigate to your project folder. That's it, You're ready to go! ## Datasets Half of the dataset used in this project is from the [FaceForensics](https://github.com/ondyari/FaceForensics/tree/master/dataset) deepfake detection dataset. . To download this data, please make sure to fill out the [google form](https://github.com/ondyari/FaceForensics/#access) to request access to the data. For the dataset that we collected from Youtube, it is accessible on [S3](ttps://deepfake-detection.s3.amazonaws.com/augment_deepfake.tar.gz) for download. To automatically download and restructure both datasets, please execute: ``` bash restructure_data.sh faceforensics_download.py ``` Note: You need to have received the download script from FaceForensics++ people before executing the restructure script. Note2: We created the `restructure_data.sh` to do a split that replicates our exact experiments avaiable in the UI above, please feel free to change the splits as you wish. ## Walkthrough Before starting to train/evaluate models, we should first create the docker image that we will be running our experiments with. To do so, we already prepared a dockerfile to do that inside `custom_docker_image`. To create the docker image, execute the following commands in terminal: ``` cd custom_docker_image nvidia-docker build . -t atlas_ff ``` Note: if you change the image name, please make sure you also modify line 16 of `job.config.yaml` to match the docker image name. Inside `job.config.yaml`, please modify the data path on host from `/media/biggie2/FaceForensics/datasets/` to the absolute path of your `datasets` folder. The folder containing your datasets should have the following structure: ``` datasets ├── augment_deepfake (2) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── base_deepfake (1) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── both_deepfake (3) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── precomputed (4) └── T_deepfake (0) ├── manipulated_sequences │ ├── DeepFakeDetection │ ├── Deepfakes │ ├── Face2Face │ ├── FaceSwap │ └── NeuralTextures └── original_sequences ├── actors └── youtube ``` Notes: * (0) is the dataset downloaded using the FaceForensics repo scripts * (1) is a reshaped version of FaceForensics data to match the expected structure by the codebase. subfolders called `frames` contain frames collected using `ffmpeg` * (2) is the augmented dataset, collected from youtube, available on s3. * (3) is the combination of both base and augmented datasets. * (4) precomputed will be automatically created during training. It holds cashed cropped frames. Then, to run all the experiments we will show in the article to come, you can launch the script `hparams_search.py` using: ```bash python hparams_search.py ``` ## Results In the following pictures, the title for each subplot is in the form `real_prob, fake_prob | prediction | label`. #### Model trained on FaceForensics++ dataset For models trained on the paper dataset alone, we notice that the model only learns to detect the manipulation techniques mentioned in the paper and misses all the manipulations in real world data (from data)   #### Model trained on Youtube dataset Models trained on the youtube data alone learn to detect real world deepfakes, but also learn to detect easy deepfakes in the paper dataset as well. These models however fail to detect any other type of manipulation (such as NeuralTextures).   #### Model trained on Paper + Youtube dataset Finally, models trained on the combination of both datasets together, learns to detect both real world manipulation techniques as well as the other methods mentioned in FaceForensics++ paper.   for a more in depth explanation of these results, please refer to the [article](https://www.dessa.com/post/deepfake-detection-that-actually-works) we published. More results can be seen in the [interactive UI](http://deepfake-detection.dessa.com/projects) ## Help improve this technology Please feel free to fork this work and keep pushing on it. If you also want to help improving the deepfake detection datasets, please share your real/forged samples at foundations@dessa.com. ## LICENSE © 2020 Square, Inc. ATLAS, DESSA, the Dessa Logo, and others are trademarks of Square, Inc. All third party names and trademarks are properties of their respective owners and are used for identification purposes only.
FaceForensics
Github of the FaceForensics dataset
Awesome-Deepfakes-Detection
A list of tools, papers and code related to Deepfake Detection.
interviews
Everything you need to know to get the job.
coding-interview-university
A complete computer science study plan to become a software engineer.