DeepFake is composed from Deep Learning and Fake and means taking one person from an image or video and replacing with someone else likeness using technology such as Deep Artificial Neural Networks [1]. Large companies like Google invest very much in fighting the DeepFake, this including release of large datasets to help training models to counter this threat [2].The phenomen invades rapidly the film industry and threatens to compromise news agencies. Large digital companies, including content providers and social platforms are in the frontrun of fighting Deep Fakes. GANs that generate DeepFakes becomes better every day and, of course, if you include in a new GAN model all the information we collected until now how to combat various existent models, we create a model that cannot be beatten by the existing ones.
In the Data Exploration section we perform a (partial) Exploratory Data Analysis (EDA) on the training and testing data. After we are checking the files types, we are focusing first on the metadata files, which we are exploring in details, after we are importing in dataframes. Then, we move to explore video files, by looking first to a sample of fake videos, then to real videos. After that, we are also exploring few of the videos with the same origin. We are visualizing one frame extracted from the video, for both real and fake videos. Then we are also playing few videos.
Then, we move to perform face (and other objects from the persons in the videos) extraction. More precisely, we are using OpenCV Haar Cascade resources to identify frontal face, eyes, smile and profile face from still images in the videos.
Link to source code zip file: [ Ссылка ]
Source code: [ Ссылка ]
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In this video, we'll use chatgpt to generate text summaries of the videos we've watched. By doing this, we'll be able to extract important information from the videos and learn how to use chatgpt to generate text summaries of videos. So join us in this tutorial and see how chatgpt can help us summarize videos and learn more about them!
