|
- How to identify Deepfake Videos - The Windows Club
Deepfake technology is used to create highly manipulated media This article shows the best ways to identify the Deepfake videos
- Deepfake video detection methods, approaches, and challenges
This research encloses a precise review of deepfake video detection procedures accentuating the potential strengths and flaws of feature-based, audio-based, and video-based detection models and multi-modal techniques
- Deepfake video deception detection using visual attention . . . - Nature
CNN based models were employed in most recent studies to identify these manipulations For example, the foundation for creating such detection models is pre-trained Capsule Network 7 and Xception 8
- Detecting deepfake videos: an enhanced hybrid deep learning model
In this article, we present a novel hybrid deep learning model designed for the efficient detection of deepfakes in videos using the Transfer Learning technique Recognizing that both spatial and temporal features are critical to detection performance, our model integrates CNN, RNN, Inception and Xception architectures
- Deepfake Videos Are More Realistic Than Ever. Heres How to Spot if a . . .
Here's a guide to the tell-tale signs of an AI-generated video, even when it looks real
- How to detect Deepfakes using AI? - GeeksforGeeks
This multi-faceted approach allows for more comprehensive detection, as different components of the model can specialize in identifying various aspects of deepfake manipulation Hybrid models are especially valuable in detecting complex deepfakes that involve both image and video manipulation
- An efficient deepfake video detection using robust deep learning
Although there have been various attempts to identify deep fake videos, these approaches are not universal Identifying these misleading deepfakes is the first step in preventing them from spreading on social media sites We introduce a unique deep-learning technique to identify fraudulent clips
- Unmasking Deep Fakes: Leveraging Deep Learning for Video Authenticity . . .
In this paper, we consider using MTCNN as a face detector and EfficientNet-B5 as encoder model to predict if a video is deepfake or not We utilize training and evaluation dataset from Kaggle DFDC
|
|
|