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Misleading Deep-Fake Detection with GAN Fingerprints

Misleading Deep-Fake Detection with GAN Fingerprints

25 May 2022
Vera Wesselkamp
Konrad Rieck
Dan Arp
Erwin Quiring
    AAML
ArXivPDFHTML

Papers citing "Misleading Deep-Fake Detection with GAN Fingerprints"

22 / 22 papers shown
Title
Spectral Distribution Aware Image Generation
Spectral Distribution Aware Image Generation
Steffen Jung
Margret Keuper
OOD
44
34
0
05 Dec 2020
Adversarial Threats to DeepFake Detection: A Practical Perspective
Adversarial Threats to DeepFake Detection: A Practical Perspective
Paarth Neekhara
Brian Dolhansky
Joanna Bitton
Cristian Canton Ferrer
AAML
18
81
0
19 Nov 2020
Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware
  Clues
Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues
Yuyang Qian
Guojun Yin
Lu Sheng
Zixuan Chen
Jing Shao
CVBM
86
677
0
18 Jul 2020
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow
  Reconstruction
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction
Yihao Huang
Felix Juefei Xu
Run Wang
Qing Guo
Lei Ma
Xiaofei Xie
Jianwen Li
Weikai Miao
Yang Liu
G. Pu
36
70
0
13 Jun 2020
Preliminary Forensics Analysis of DeepFake Images
Preliminary Forensics Analysis of DeepFake Images
Luca Guarnera
O. Giudice
Cristina Nastasi
Sebastiano Battiato
CVBM
30
36
0
27 Apr 2020
Evading Deepfake-Image Detectors with White- and Black-Box Attacks
Evading Deepfake-Image Detectors with White- and Black-Box Attacks
Nicholas Carlini
Hany Farid
AAML
26
147
0
01 Apr 2020
Leveraging Frequency Analysis for Deep Fake Image Recognition
Leveraging Frequency Analysis for Deep Fake Image Recognition
Joel Frank
Thorsten Eisenhofer
Lea Schonherr
Asja Fischer
D. Kolossa
Thorsten Holz
28
546
0
19 Mar 2020
Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are
  Failing to Reproduce Spectral Distributions
Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions
Ricard Durall
Margret Keuper
J. Keuper
43
333
0
03 Mar 2020
Media Forensics and DeepFakes: an overview
Media Forensics and DeepFakes: an overview
L. Verdoliva
68
544
0
18 Jan 2020
CNN-generated images are surprisingly easy to spot... for now
CNN-generated images are surprisingly easy to spot... for now
Sheng-Yu Wang
Oliver Wang
Richard Y. Zhang
Andrew Owens
Alexei A. Efros
OOD
112
965
0
23 Dec 2019
Analyzing and Improving the Image Quality of StyleGAN
Analyzing and Improving the Image Quality of StyleGAN
Tero Karras
S. Laine
M. Aittala
Janne Hellsten
J. Lehtinen
Timo Aila
GAN
252
5,769
0
03 Dec 2019
SpoC: Spoofing Camera Fingerprints
SpoC: Spoofing Camera Fingerprints
D. Cozzolino
Justus Thies
Andreas Rossler
Matthias Nießner
L. Verdoliva
53
39
0
27 Nov 2019
GANprintR: Improved Fakes and Evaluation of the State of the Art in Face
  Manipulation Detection
GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection
João C. Neves
Ruben Tolosana
R. Vera-Rodríguez
Vasco Lopes
Hugo Proencca
Julian Fierrez
AAML
PICV
44
130
0
13 Nov 2019
Unmasking DeepFakes with simple Features
Unmasking DeepFakes with simple Features
Ricard Durall
Margret Keuper
Franz-Josef Pfreundt
J. Keuper
CVBM
45
217
0
02 Nov 2019
Detecting and Simulating Artifacts in GAN Fake Images
Detecting and Simulating Artifacts in GAN Fake Images
Xu-Yao Zhang
Svebor Karaman
Shih-Fu Chang
79
481
0
15 Jul 2019
Do GANs leave artificial fingerprints?
Do GANs leave artificial fingerprints?
Francesco Marra
Diego Gragnaniello
L. Verdoliva
Giovanni Poggi
GAN
44
321
0
31 Dec 2018
Spectral Normalization for Generative Adversarial Networks
Spectral Normalization for Generative Adversarial Networks
Takeru Miyato
Toshiki Kataoka
Masanori Koyama
Yuichi Yoshida
ODL
127
4,421
0
16 Feb 2018
Demystifying MMD GANs
Demystifying MMD GANs
Mikolaj Binkowski
Danica J. Sutherland
Michael Arbel
Arthur Gretton
EGVM
77
1,470
0
04 Jan 2018
Progressive Growing of GANs for Improved Quality, Stability, and
  Variation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Tero Karras
Timo Aila
S. Laine
J. Lehtinen
GAN
100
7,318
0
27 Oct 2017
The Cramer Distance as a Solution to Biased Wasserstein Gradients
The Cramer Distance as a Solution to Biased Wasserstein Gradients
Marc G. Bellemare
Ivo Danihelka
Will Dabney
S. Mohamed
Balaji Lakshminarayanan
Stephan Hoyer
Rémi Munos
GAN
40
344
0
30 May 2017
LSUN: Construction of a Large-scale Image Dataset using Deep Learning
  with Humans in the Loop
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
Feng Yu
Ari Seff
Yinda Zhang
Shuran Song
Thomas Funkhouser
Jianxiong Xiao
39
2,320
0
10 Jun 2015
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAML
GAN
158
18,922
0
20 Dec 2014
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