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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
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