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Deepfake这个词是“深度学习”和“伪造假冒（fake）”的组合。一般来说，它指的是由人工智能生成的、现实生活中不存在的人或物体，它们看上去是逼真的。Deepfake的最常见形式是人类图像的生成和操控。例如，对外国电影进行逼真的视频配音，在购物时虚拟地穿上衣服，对演员进行换脸等等。

稍具体内容介绍可参见：

【无中生有的AI】关于deepfake的入门级梳理

以及本次推送第二条公众号文章：deepfake中文综述《视听觉深度伪造检测技术研究综述》

笔者下载了2018年至2020年5月的40多篇deepfake相关论文，供有兴趣的小伙伴做了解，公众号后台回复 df 即可获取所有论文。

新：

1、DeepFaceLab: A simple, flexible and extensible face swapping framework

https://arxiv.org/pdf/2005.05535.pdf

2、DeepFake: Deep Dueling-based Deception Strategy to Defeat Reactive Jammers

https://arxiv.org/pdf/2005.07034.pdf

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

https://arxiv.org/pdf/2005.13770.pdf

4、Not made for each other– Audio-Visual Dissonance-based Deepfake Detection and Localization

https://arxiv.org/pdf/2005.14405.pdf

5、Fake Face Detection via Adaptive Residuals Extraction Network

https://arxiv.org/pdf/2005.04945.pdf

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001  (2020-05-1) Deepfake Forensics Using Recurrent Neural Networks

https://arxiv.org/pdf/2005.00229.pdf

002  (2020-04-29) Deepfake Video Forensics based on Transfer Learning

https://arxiv.org/pdf/2004.14178.pdf

003  (2020-04-27) Preliminary Forensics Analysis of DeepFake Images

https://arxiv.org/pdf/2004.12626.pdf

004  (2020-04-24) Deepfakes Detection with Automatic Face Weighting

https://arxiv.org/pdf/2004.12027.pdf

005  (2020-04-23) The Creation and Detection of Deepfakes  A Survey

https://arxiv.org/pdf/2004.11138.pdf

006  (2020-04-22) DeepFake Detection by Analyzing Convolutional Traces

https://arxiv.org/pdf/2004.10448.pdf

007  (2020-04-16) Video Face Manipulation Detection Through Ensemble of CNNs

https://arxiv.org/pdf/2004.07676.pdf

008  (2020-04-16) DeepFakes Evolution  Analysis of Facial Regions and Fake Detection Performance

https://arxiv.org/pdf/2004.07532.pdf

009  (2020-04-1) Evading Deepfake-Image Detectors with White- and Black-Box Attacks

https://arxiv.org/pdf/2004.00622.pdf

010  (2020-03-23) Adversarial Perturbations Fool Deepfake Detectors

https://arxiv.org/pdf/2003.10596.pdf

011  (2020-03-11) DeepFake Detection  Current Challenges and Next Steps

https://arxiv.org/pdf/2003.09234.pdf

012  (2020-03-19) Detecting Deepfakes with Metric Learning

https://arxiv.org/pdf/2003.08645.pdf

013  (2020-03-17) Emotions Don't Lie  A Deepfake Detection Method using Audio-Visual Affective Cues

https://arxiv.org/pdf/2003.06711.pdf

014  (2020-03-3) Watch your Up-Convolution  CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions

https://arxiv.org/pdf/2003.01826.pdf

015  (2020-04-27) Disrupting Deepfakes  Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems

https://arxiv.org/pdf/2003.01279.pdf

016  (2020-02-7) Deepfakes for Medical Video De-Identification  Privacy Protection and Diagnostic Information Preservation

https://arxiv.org/pdf/2003.00813.pdf

017  (2020-03-13) Adversarial Deepfakes  Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples

https://arxiv.org/pdf/2002.12749.pdf

018  (2020-05-4) Amplifying The Uncanny

https://arxiv.org/pdf/2002.06890.pdf

019  (2020-01-21) Detecting Face2Face Facial Reenactment in Videos

https://arxiv.org/pdf/2001.07444.pdf

020  (2020-01-17) Media Forensics and DeepFakes  an overview

https://arxiv.org/pdf/2001.06564.pdf

021  (2020-02-21) Advbox  a toolbox to generate adversarial examples that fool neural networks

https://arxiv.org/pdf/2001.05574.pdf

022  (2020-01-5) FDFtNet  Facing Off Fake Images using Fake Detection Fine-tuning Network

https://arxiv.org/pdf/2001.01265.pdf

023  (2020-01-1) DeepFakes and Beyond  A Survey of Face Manipulation and Fake Detection

https://arxiv.org/pdf/2001.00179.pdf

024  (2020-04-18) Face X-ray for More General Face Forgery Detection

https://arxiv.org/pdf/1912.13458.pdf

025  (2019-12-21) Detecting Deepfake-Forged Contents with Separable Convolutional Neural Network and Image Segmentation

https://arxiv.org/pdf/1912.12184.pdf

026  (2020-04-4) CNN-generated images are surprisingly easy to spot... for now

https://arxiv.org/pdf/1912.11035.pdf

027  (2020-03-4) Unmasking DeepFakes with simple Features

https://arxiv.org/pdf/1911.00686.pdf

028  (2019-10-29) Use of a Capsule Network to Detect Fake Images and Videos

https://arxiv.org/pdf/1910.12467.pdf

029  (2019-10-23) The Deepfake Detection Challenge (DFDC) Preview Dataset

https://arxiv.org/pdf/1910.08854.pdf

030  (2019-10-9) Adversarial Learning of Deepfakes in Accounting

https://arxiv.org/pdf/1910.03810.pdf

031  (2019-10-3) Vulnerability of Face Recognition to Deep Morphing

https://arxiv.org/pdf/1910.01933.pdf

032  (2020-03-16) Celeb-DF  A Large-scale Challenging Dataset for DeepFake Forensics

https://arxiv.org/pdf/1909.12962.pdf

033  (2019-09-25) Deep Learning for Deepfakes Creation and Detection

https://arxiv.org/pdf/1909.11573.pdf

034  (2019-05-9) Limits of Deepfake Detection  A Robust Estimation Viewpoint

https://arxiv.org/pdf/1905.03493.pdf

035  (2019-05-16) Recurrent Convolutional Strategies for Face Manipulation Detection in Videos

https://arxiv.org/pdf/1905.00582.pdf

036  (2019-10-20) Fake News Disinformation and Deepfakes  Leveraging Distributed Ledger Technologies and Blockchain to Combat Digital Deception and Counterfeit Reality

https://arxiv.org/pdf/1904.05386.pdf

037  (2019-10-2) Detecting GAN generated Fake Images using Co-occurrence Matrices

https://arxiv.org/pdf/1903.06836.pdf

038  (2019-08-26) FaceForensics++  Learning to Detect Manipulated Facial Images

https://arxiv.org/pdf/1901.08971.pdf

039  (2018-12-20) DeepFakes  a New Threat to Face Recognition  Assessment and Detection

https://arxiv.org/pdf/1812.08685.pdf

040  (2018-12-19) Detecting GAN-generated Imagery using Color Cues

https://arxiv.org/pdf/1812.08247.pdf

041  (2019-05-22) Exposing DeepFake Videos By Detecting Face Warping Artifacts

https://arxiv.org/pdf/1811.00656.pdf

042  (2018-09-4) MesoNet  a Compact Facial Video Forgery Detection Network

https://arxiv.org/pdf/1809.00888.pdf

043  (2018-06-11) In Ictu Oculi  Exposing AI Generated Fake Face Videos by Detecting Eye Blinking

https://arxiv.org/pdf/1806.02877.pdf