https://www.youtube.com/watch?v=SH2ciNvcmMU
Yes, I did this, why? Cause I was bored-The hashtag is #InfernoOutfitBattleSong…https://youtu.be/tzKGp--WH9kThumbnail…https://youtu.be/9jfbUocUXI4Tools used…

https://www.youtube.com/watch?v=Rmr88MC8mJc
💖HI WELCOME TO THE DESCRIPTION 💖🌸Here is the Fnf vid I used🌸https://youtu.be/2KIBMNozDqQ?si=EjgPhfKy6IAkDvpK⭐How to Join this fake Collab⭐#1- go to

https://www.youtube.com/watch?v=a6f5tSFf77o
participants who are in the video: @-Fnaf_GACHA_truama-

https://paperswithcode.com/dataset/dfdc
The DFDC (Deepfake Detection Challenge) is a dataset for deepface detection consisting of more than 100,000 videos. The DFDC dataset consists of two versions: Preview dataset. with 5k videos. Featuring two facial modification algorithms. Full dataset, with 124k videos. Featuring eight facial modification algorithms

https://ai.meta.com/datasets/dfdc/
Overview. We partnered with other industry leaders and academic experts in September 2019 to create the Deepfake Detection Challenge (DFDC) in order to accelerate development of new ways to detect deepfake videos. In doing so, we created and shared a unique new dataset for the challenge consisting of more than 100,000 videos.

https://paperswithcode.com/dataset/celeb-df
Celeb-DF. Introduced by Li et al. in Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics. Celeb-DF is a large-scale challenging dataset for deepfake forensics. It includes 590 original videos collected from YouTube with subjects of different ages, ethnic groups and genders, and 5639 corresponding DeepFake videos.

https://www.kaggle.com/c/deepfake-detection-challenge
If the issue persists, it's likely a problem on our side. Unexpected token < in JSON at position 4.

https://ai.meta.com/blog/deepfake-detection-challenge-results-an-open-initiative-to-advance-ai/
Share on Twitter. We're sharing results of the Deepfake Detection Challenge, an open, collaborative initiative to spur creation of innovative new technologies to detect deepfakes and manipulated media. The competition drew more than 2,000 participants, who trained and tested their models using a unique new dataset created for the challenge.

https://paperswithcode.com/task/deepfake-detection/latest
**DeepFake Detection** is the task of detecting fake videos or images that have been generated using deep learning techniques. Deepfakes are created by using machine learning algorithms to manipulate or replace parts of an original video or image, such as the face of a person. The goal of deepfake detection is to identify such manipulations and distinguish them from real videos or images.

https://github.com/selimsef/dfdc_deepfake_challenge
As I computed fake loss and real loss separately inside each batch, results might be better with larger batch size, for example on V100 gpus. Even though SyncBN is used larger batch on each GPU will lead to less noise as DFDC dataset has some fakes where face detector failed and face crops are not really fakes.

https://deepai.org/publication/the-deepfake-detection-challenge-dataset
The DFDC dataset is by far the largest currently- and publicly-available face swap video dataset, with over 100,000 total clips sourced from 3,426 paid actors, produced with several Deepfake, GAN-based, and non-learned methods. In addition to describing the methods used to construct the dataset, we provide a detailed analysis of the top

https://www.youtube.com/watch?v=UKl90tsnJ7U
i have no other idea than make another FC that looked identical to the previous one- ;-;i have no motivation to make any fnaf videos whatsoever.. sry im late

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251415
The recent advances in language modeling significantly improved the generative capabilities of deep neural models: in 2019 OpenAI released GPT-2, a pre-trained language model that can autonomously generate coherent, non-trivial and human-like text samples. Since then, ever more powerful text generative models have been developed. Adversaries can exploit these tremendous generative capabilities

https://deepai.org/publication/the-deepfake-detection-challenge-dfdc-preview-dataset
10/19/2019. ∙. by Brian Dolhansky, et al. ∙. 0. ∙. In this paper, we introduce a preview of the Deepfakes Detection Challenge (DFDC) dataset consisting of 5K videos featuring two facial modification algorithms. A data collection campaign has been carried out where participating actors have entered into an agreement to the use and

https://medium.com/analytics-vidhya/training-a-model-for-custom-object-detection-tf-2-x-on-google-colab-4507f2cc6b80
In this tutorial, I will be training a Deep Learning model for custom object detection using TensorFlow 2.x on Google Colab. Following is the roadmap for it. Roadmap. Collect the dataset of images

https://paperswithcode.com/paper/the-deepfake-detection-challenge-dataset
The DFDC dataset is by far the largest currently and publicly available face swap video dataset, with over 100,000 total clips sourced from 3,426 paid actors, produced with several Deepfake, GAN-based, and non-learned methods. In addition to describing the methods used to construct the dataset, we provide a detailed analysis of the top

https://www.youtube.com/watch?v=TCe1Vqb3TW4
Please get this to atleast 1k views! It took me days...#outfitbattle #gacha #collab #fakecollab #gachaclub #funny #shortsOriginal : https://www.youtube.com/w

https://ai.meta.com/blog/deepfake-detection-challenge/
The Deepfake Detection Challenge will include a dataset and leaderboard, as well as grants and awards, to spur the industry to create new ways of detecting and preventing media manipulated via AI from being used to mislead others. The governance of the challenge will be facilitated and overseen by the Partnership on AI's new Steering

https://colab.research.google.com/github/Mochoye/Deepfake-with-Google-Colab/blob/main/Deepfake.ipynb
DeepFake. Deepfake is a technology that uses artificial intelligence to manipulate the appearance and voice of a person in a video. Roop uses a face swapping technique that replaces the original face in the video with the desired face, while preserving the facial expressions and movements.

https://pub.towardsai.net/deep-fake-detection-using-opencv-and-mtcnn-833625abdd03
Matplotlib: It is used for plotting the detection results (histograms and ROC. curves, etc.). Datasets. We are using the DeepfakeTIMIT database, a database of videos where faces are swapped using the open-source GAN-based approach. This dataset was created from the original autoencoder-based Deepfake algorithm.

https://paperswithcode.com/paper/the-deepfake-detection-challenge-dfdc-preview
Edit social preview. In this paper, we introduce a preview of the Deepfakes Detection Challenge (DFDC) dataset consisting of 5K videos featuring two facial modification algorithms. A data collection campaign has been carried out where participating actors have entered into an agreement to the use and manipulation of their likenesses in our

https://www.youtube.com/watch?v=c0PNdiFcpgw
Hello! #ItsNotMeFakeCollab Enjoy guys!!! @♡- 𝘈 𝘒 𝘐 𝘙 𝘈 -♡

https://towardsdatascience.com/realistic-deepfakes-colab-e13ef7b2bba7
Generate Deepfakes with a single image. Image animation aims to generate video sequences such that the person in the source image is animated according to the motion of a video. This technology lies within the field of computer vision, and AI researchers have been working on to produce more realistic videos. It leverages on machine learning to