3 research outputs found
Global Texture Enhancement for Fake Face Detection in the Wild
Generative Adversarial Networks (GANs) can generate realistic fake face
images that can easily fool human beings.On the contrary, a common
Convolutional Neural Network(CNN) discriminator can achieve more than 99.9%
accuracyin discerning fake/real images. In this paper, we conduct an empirical
study on fake/real faces, and have two important observations: firstly, the
texture of fake faces is substantially different from real ones; secondly,
global texture statistics are more robust to image editing and transferable to
fake faces from different GANs and datasets. Motivated by the above
observations, we propose a new architecture coined as Gram-Net, which leverages
global image texture representations for robust fake image detection.
Experimental results on several datasets demonstrate that our Gram-Net
outperforms existing approaches. Especially, our Gram-Netis more robust to
image editings, e.g. down-sampling, JPEG compression, blur, and noise. More
importantly, our Gram-Net generalizes significantly better in detecting fake
faces from GAN models not seen in the training phase and can perform decently
in detecting fake natural images
Classification of Real and Fake Human Faces Using Deep Learning
Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep learning is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%