Nowadays, everyone is glued to the many resources that are available online because the Internet is one of the most used technologies in the modern period. News and other resources are accessible on a number of online channels, including Facebook, Twitter, and WhatsApp. The quick dissemination of news in a relatively short period of time is due to social media platforms. It is impossible to tell whether the news that is being circulated among many people is true or false. Therefore, the effects of this false information are numerous. One of the biggest problems in the present day is fake news, which can affect judgements. Detecting fake videos can be a challenging problem, since they can be created with sophisticated techniques that make it difficult to distinguish between real and fake videos. However, deep learning algorithms can be effective in detecting fake videos by learning patterns in the data. The numerous cutting-edge technologies, including machine learning algorithms, can be used to combat fake news as a consequence. This paper proposes a novel framework for determining if news is fake or not using a variety of natural language processing techniques. The several methods that have been utilized, such as deep neural network (DNN), logistic regression (LR), random forest (RF), naive bay classifier (NBC), and web scraping (WS), were used to identify fake news. In this work, a self-collected dataset of more than 200 news videos from diverse sources was employed. The deep neural network model has the highest accuracy, at 98%, of all the models. Fake video news detection using deep learning algorithms is an active area of research and development. Deep learning, a subset of machine learning, has shown promising results in various domains, including computer vision, natural language processing, and speech recognition. When it comes to detecting fake videos, deep learning algorithms can be employed to analyze the visual content and identify signs of manipulation or forgery.
Here are some common approaches used in fake video news detection using deep learning:
Frame-level analysis: In this approach, each frame of a video is analyzed independently to identify visual artifacts or inconsistencies that may indicate tampering. Convolutional Neural Networks (CNNs) are commonly used for this task, as they are effective in image analysis. By training the CNN on a dataset of real and manipulated video frames, it can learn to differentiate between genuine and fake frames.
Spatio-temporal analysis: This approach takes into account both the spatial information within individual frames and the temporal information across consecutive frames. Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM) networks, can be used to capture the temporal dependencies in videos. By analyzing the motion patterns, object movements, and consistency of visual features over time, the algorithm can detect potential manipulations or inconsistencies.
Deepfake detection: Deepfakes are videos that use deep learning techniques to generate highly realistic but fake content by swapping faces or altering the actions of individuals. Deepfake detection algorithms typically involve training deep neural networks on a large dataset of both real and deepfake videos. These models can learn to identify subtle artifacts or irregularities introduced by deepfake generation methods.
Audiovisual analysis: Fake video news detection can also incorporate audio analysis along with visual analysis. Deep learning algorithms can be used to analyze the audio content of a video and compare it with the visual information. Mismatches between the audio and visual components can indicate potential manipulations.
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