65 skills found · Page 2 of 3
bexway / Outloook Calendar ReaderA python script that uses pywincom to read a Microsoft Outlook Calendar and build a csv file of the scheduled appointments
ajaybhatiya1234 / 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.
mrlrch / MalwariconBash-based recon automation script that orchestrates tools like Nmap, Dirsearch (Python), and crt.sh to perform port scanning, directory enumeration, certificate parsing, and generate structured reports. Supports multiple domains, scan mode selection, interactive input, and cron scheduling for continuous monitoring.
shanelawrence / Cve ManagerA collection of python apps and shell scripts to email an xlsx spreadsheet of new vulnerabilities in the NIST CVE database and their associated products on a daily schedule.
sungchun12 / Schedule Python Script Using Google Cloud:clock4: Schedules a Python script to append data into Bigquery using Google Cloud's App Engine with a cron job
lukethacoder / Spotify Playlist Backup🎵 Scheduled Python script to backup your personal Spotify playlists incase the platform ever goes down (or you just like having your data).
fronzbot / Docker PycronConfigurable scheduling for custom python scripts
sherman5 / FantasyFootballScheduleAnalyzerPython script for analyzing the effect your fantasy schedule has on the league standings
rawheel / Zoom Bot To Take Online ClassesA python zoom bot which automates the process of joining zoom meeting. Just left you pc open the script will run automatically at the particular scheduled time.
sajag1999 / Website Blocker ProjectThis Website blocking project is competed using python.It will be very useful ,while working in offices(working hours). For to start ,you need to find the path of the local host script and creat a task in task scheduler.
obinnakenan / Python AutomationA collection of Python-based automation scripts for various tasks, from data cleaning and scraping to report generation and scheduling
PatSunter / SimpleGTFSCreatorPython library and scripts to help create a simple GTFS schedule from GIS files and minimal speed & headway information.
Astha132005 / Automated WhatsApp MessagesA Python script to send automated WhatsApp messages at a scheduled time using `pywhatkit`. Ideal for reminders, greetings, or alerts. Simple and effective for beginners exploring automation with Python.
cheesebanana / YellowstackReal-time Python script runner with scheduling, logging, and OpenAI-assisted debugging
ilmalte / Github Actions With SqliteA Typescript page, compiled with Web Assembly and dynamically deployed on Github Pages. It displays random data from a sqlite database, populated by a simple Python script scheduled with Github Actions.
socaltiger / BatchSubmit.comA lightweight Distributed Asynchronous Job Submitting/Scheduling Platform for SAS/R/Python and/or other scripts with web interface capable of storing parameters, competes with AWS Batch/AWS Step Functions, Google Cloud Workflows/Cloud Tasks, Azure Batch, IBM Cloud Schematics/DataStage, port-to-Python, help-wanted, good-first-issue, legacy-Perl
daemonio / Reddit PosterPython scripts to schedule posting in reddit. It includes a simple heuristic to schedule the best time to post.
KouzanaFedi / IssatSo ScrapperA python script to scrap schedules from the IssatSo university website.
evancg37 / DoorDash SchedulerA Python script that can use an Android emulator to automatically schedule a shift on the DoorDash Dasher app for a specific time frame
ftsiadimos / BashTowerBash Tower is a streamlined web interface for executing Bash/Python scripts across multiple remote servers via SSH. It simplifies day-to-day sysadmin tasks by providing a centralized dashboard for templates, scheduling (Cron), and host management without the overhead of heavy configuration management tools.
