Fetching the paper…
Reading the bibliography…
Thanks to the fast progress in synthetic media generation, creating realistic false images has become very easy.
Digital camera identification from sensor pattern noise
Jan Lukàš, Jessica Fridrich, and Miroslav Goljan · 2006
Earlier work this paper cites.
Determining image origin and integrity using sensor noise
Mo Chen, Jessica Fridrich, Miroslav Goljan, and Jan Lukàš · 2008
Earlier work this paper cites.
Synthesis of color filter array pattern in digital images
Matthias Kirchner and Rainer Böhme · 2009
Earlier work this paper cites.
Image forgery localization via fine-grained analysis of CFA artifacts
Pasquale Ferrara, Tiziano Bianchi, Alessia De Rosa, and Alessandro Piva · 2012
Earlier work this paper cites.
Exposing Region Splicing Forgeries with Blind Local Noise Estimation
Siwei Lyu, Xunyu Pan, and Xing Zhang · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Forensic camera model identification
Matthias Kirchner and Thomas Gloe · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Face2Face: Real-Time Face Capture and Reenactment of RGB Videos
Justus Thies, Michael Zollhöfer, Marc Stamminger, Christian Theobalt, and Matthias Nießner · 2016
Earlier work this paper cites.
Camera model identification with the use of deep convolutional neural networks
Amel Tuama, Frédéric Comby, and Marc Chaumont · 2016
Earlier work this paper cites.
Photo Forensics from JPEG Dimples
Shruti Agarwal and Hany Farid · 2017
Earlier work this paper cites.
First steps toward camera model identification with convolutional neural networks
Luca Bondi, Luca Baroffio, David Güera, Paolo Bestagini, Edward Delp, and Stefano Tubaro · 2017
Earlier work this paper cites.
Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
Earlier work this paper cites.
A Counter-Forensic Method for CNN-Based Camera Model Identification
David Güera, Yu Wang, Luca Bondi, Paolo Bestagini, Stefano Tubaro, and Edward Delp · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger · 2017
Earlier work this paper cites.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros · 2017
Earlier work this paper cites.
Novel visual and statistical image features for microblogs news verification
Zhiwei Jin, Juan Cao, Yongdong Zhang, Jianshe Zhou, and Qi Tian · 2017
Earlier work this paper cites.
Delving into tranferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Shanghai Jiao Tong, Chang Liu, and Dawn Song · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 2017
Cited alongside, same era.
On the Transferability of Adversarial Examples against CNN-based Image Forensics
Mauro Barni, Kassem Kallas, Ehsan Nowroozi, and Benedetta Tondi · 2018
Cited alongside, same era.
Adversarial multimedia forensics: Overview and challenges ahead
Mauro Barni, Matthew Stamm, and Benedetta Tondi · 2018
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis, 2018
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
Cited alongside, same era.
Do GANs leave artificial fingerprints?
Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi · 2019
Closest in time.
Exploiting visual artifacts to expose deepfakes and face manipulations
Falko Matern, Christian Riess, and Mark Stamminger · 2019
Closest in time.
FSGAN: Subject Agnostic Face Swapping and Reenactment
Yuval Nirkin, Yosi Keller, and Tal Hassner · 2019
Closest in time.
Make a Face: Towards Arbitrary High Fidelity Face Manipulation
Shengju Qian, Kwan-Yee Lin, Wayne Wu, Yangxiaokang Liu, Quan Wang, Fumin Shen, Chen Qian, and Ran He · 2019
Closest in time.
FaceForensics++: Learning to Detect Manipulated Facial Images
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner · 2019
Closest in time.
AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo · 2018
Cited alongside, same era.
Analysis of adversarial attacks against CNN-based image forgery detectors
Diego Gragnaniello, Francesco Marra, Giovanni Poggi, and Luisa Verdoliva · 2018
Cited alongside, same era.
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Cited alongside, same era.
Detection of GAN-Generated Fake Images over Social Networks
Francesco Marra, Diego Gragnaniello, Giovanni Poggi, and Luisa Verdoliva · 2018
Cited alongside, same era.
On the vulnerability of deep learning to adversarial attacks for camera model identification
Francesco Marra, Diego Gragnaniello, and Luisa Verdoliva · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Cited alongside, same era.
RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces
Ryota Natsume, Tatsuya Yatagawa, and Shigeo Morishim · 2018
Cited alongside, same era.
Xiaosen Wang, Kun He, and John E. Hopcroft · 2019
Closest in time.
Relgan: Multi-domain image-to-image translation via relative attributes
Po-Wei Wu, Yu-Jing Lin, Che-Han Chang, Edward Y. Chang, and Shih-Wei Liao · 2019
Closest in time.
Exposing GAN-synthesized Faces using Landmark Locations
Xin Yang, Yuezun Li, Honggang Qi, and Siwei Lyu · 2019
Closest in time.
Detecting and Simulating Artifacts in GAN Fake Images
Xu Zhang, Svebor Karaman, and Shih-Fu Chang · 2019
Closest in time.
Evading deepfake-image detectors with white- and black-box attacks
Nicholas Carlini and Hany Farid · 2020
Closest in time.
What makes fake images detectable? understanding properties that generalize
Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola · 2020
Closest in time.
Camera trace erasing
Chang Chen, Zhiwei Xiong, Xiaoming Liu, and Feng Wu · 2020
Closest in time.
Noiseprint: a CNN-based camera model fingerprint
Davide Cozzolino and Luisa Verdoliva · 2020
Closest in time.
On the detection of digital face manipulation
Hao Dang, Feng Liu, Joel Stehouwer, Xiaoming Liu, and Anil K Jain · 2020
Closest in time.
Advfaces: Adversarial face synthesis
Debayan Deb, Jianbang Zhang, and Anil K. Jain · 2020
Closest in time.
Analyzing and improving the image quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Closest in time.
Ganprintr: Improved fakes and evaluation of the state of the art in face manipulation detection
João C. Neves, Ruben Tolosana, Ruben Vera-Rodriguez, Vasco Lopes, Hugo Proença, and Julian Fierrez · 2020
Closest in time.
Media forensics and deepfakes: An overview
Luisa Verdoliva · 2020
Closest in time.
CNN-generated images are surprisingly easy to spot… for now
Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros · 2020
Closest in time.
Are GAN generated images easy to detect? A critical analysis of the state-of-the-art
Diego Gragnaniello, Davide Cozzolino, Francesco Marra, Giovanni Poggi, and Luisa Verdoliva · 2021
Closest in time.