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The Creation and Detection of Deepfakes: A Survey

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TLDR
This article explores the creation and detection of deepfakes and provides an in-depth view as to how these architectures work and the current trends and advancements in this domain.
Abstract
Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. In 2018, it was discovered how easy it is to use this technology for unethical and malicious applications, such as the spread of misinformation, impersonation of political leaders, and the defamation of innocent individuals. Since then, these `deepfakes' have advanced significantly. In this paper, we explore the creation and detection of deepfakes and provide an in-depth view of how these architectures work. The purpose of this survey is to provide the reader with a deeper understanding of (1) how deepfakes are created and detected, (2) the current trends and advancements in this domain, (3) the shortcomings of the current defense solutions, and (4) the areas which require further research and attention.

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Challenges in Deploying Machine Learning: a Survey of Case Studies

TL;DR: By mapping found challenges to the steps of the machine learning deployment workflow it is shown that practitioners face issues at each stage of the deployment process.
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Deep Learning for Deepfakes Creation and Detection: A Survey

TL;DR: This study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.
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LION: Latent Point Diffusion Models for 3D Shape Generation

TL;DR: The hierarchical Latent Point Diffusion Model (LION) is introduced, set up as a variational autoencoder (VAE) with a hierarchical latent space that combines a global shape latent representation with a point-structured latent space for 3D shape generation.
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Quantum State Tomography with Conditional Generative Adversarial Networks

TL;DR: This work applies conditional generative adversarial networks (CGANs) to QST and demonstrates that the QST-CGAN reconstructs optical quantum states with high fidelity, using orders of magnitude fewer iterative steps, and less data, than both accelerated projected-gradient-based and iterative maximum-likelihood estimation.
Proceedings ArticleDOI

DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voices

TL;DR: This work proposes a novel approach, named DeepSonar, based on monitoring neuron behaviors of speaker recognition system, i.e., a deep neural network (DNN), to discern AI-synthesized fake voices, and poses a new insight into adopting neuron behaviors for effective and robust AI aided multimedia fakes forensics as an inside-out approach.
References
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Journal ArticleDOI

Generative Adversarial Nets

TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Proceedings ArticleDOI

Image-to-Image Translation with Conditional Adversarial Networks

TL;DR: Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
Proceedings ArticleDOI

Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks

TL;DR: CycleGAN as discussed by the authors learns a mapping G : X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss.
Posted Content

Image-to-Image Translation with Conditional Adversarial Networks

TL;DR: Conditional Adversarial Network (CA) as discussed by the authors is a general-purpose solution to image-to-image translation problems, which can be used to synthesize photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
Proceedings ArticleDOI

FaceNet: A unified embedding for face recognition and clustering

TL;DR: A system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity, and achieves state-of-the-art face recognition performance using only 128-bytes perface.
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