11.14.4.5.1 Deepfakes, Face Synthesis, Fake News

Chapter Contents (Back)
Deepfakes.
See also Liveness Detection, Spoofing, Presentation Attack, Faces, Other Biometrics.

Cybenko, A.K., Cybenko, G.,
AI and Fake News,
IEEE_Int_Sys(33), No. 5, Sep. 2018, pp. 1-5.
IEEE DOI 1901
Cultural differences, Intelligent systems, Social network services, Psychology, Economics, Software BibRef

de Rezende, E.R.S.[Edmar R.S.], Ruppert, G.C.S.[Guilherme C.S.], Theóphilo, A.[Antônio], Tokuda, E.K.[Eric K.], Carvalho, T.[Tiago],
Exposing computer generated images by using deep convolutional neural networks,
SP:IC(66), 2018, pp. 113-126.
Elsevier DOI 1806
Digital forensics, CG detection, Deep learning, Transfer learning, Fake news BibRef

Reis, J.C.S., Correia, A., Murai, F., Veloso, A., Benevenuto, F., Cambria, E.,
Supervised Learning for Fake News Detection,
IEEE_Int_Sys(34), No. 2, March 2019, pp. 76-81.
IEEE DOI 1906
Feature extraction, Facebook, Data mining, IP networks, Radio frequency, Affective computing BibRef

Greengard, S.[Samuel],
Will Deepfakes Do Deep Damage?,
CACM(63), No. 1, January 2020, pp. 17-19.
DOI Link 1912
News item. BibRef

Strickland, E.,
Facebook takes on deepfakes,
Spectrum(57), No. 01, January 2020, pp. 40-57.
IEEE DOI 2001
They are everywhere, they are high quality. BibRef

Babaguchi, N.[Noboru], Echizen, I.[Isao], Yamagishi, J.[Junichi], Nitta, N.[Naoko], Nakashima, Y.[Yuta], Nakamura, K.[Kazuaki], Kono, K.[Kazuhiro], Fang, F.M.[Fu-Ming], Myojin, S.[Seiko], Kuang, Z.Z.[Zhen-Zhong], Nguyen, H.H.[Huy H.], Tieu, N.D.T.[Ngoc-Dung T.],
Preventing Fake Information Generation Against Media Clone Attacks,
IEICE(E104-D), No. 1, January 2021, pp. 2-11.
WWW Link. 2101
BibRef

de Oliveira, N.R., Medeiros, D.S.V., Mattos, D.M.F.,
A Sensitive Stylistic Approach to Identify Fake News on Social Networking,
SPLetters(27), 2020, pp. 1250-1254.
IEEE DOI 2007
Fake news detection, one-class SVM BibRef

Johnson, D.G.[Deborah G.], Diakopoulos, N.[Nicholas],
What To Do About Deepfakes,
CACM(64), No. 1, January 2021, pp. 33-35.
DOI Link 2103
Seeking to reap the positive uses of synthetic media while minimizing or preventing negative societal impact. BibRef

Kamboj, M., Hessler, C., Asnani, P., Riani, K., Abouelenien, M.,
Multimodal Political Deception Detection,
MultMedMag(28), No. 1, January 2021, pp. 94-102.
IEEE DOI 2104
Feature extraction, Videos, Linguistics, Visualization, Psychology, Task analysis BibRef

Mirsky, Y.[Yisroel], Lee, W.[Wenke],
The Creation and Detection of Deepfakes: A Survey,
Surveys(54), No. 1, January 2021, pp. xx-yy.
DOI Link 2104
Survey, Deepfakes. impersonation, generative AI, social engineering, face swap, replacement, deep fake, Deepfake, reenactment BibRef

Caldelli, R.[Roberto], Galteri, L.[Leonardo], Amerini, I.[Irene], del Bimbo, A.[Alberto],
Optical Flow based CNN for detection of unlearnt deepfake manipulations,
PRL(146), 2021, pp. 31-37.
Elsevier DOI 2105
BibRef
Earlier: A3, A2, A1, A4:
Deepfake Video Detection through Optical Flow Based CNN,
HBU19(1205-1207)
IEEE DOI 2004
Deepfake manipulations, Optical Flow, Video forensics, CNN. convolutional neural nets, image classification, image sequences, learning (artificial intelligence), video signal processing. BibRef

Xu, Z.P.[Zhao-Peng], Liu, J.R.[Jia-Rui], Lu, W.[Wei], Xu, B.[Bozhi], Zhao, X.F.[Xian-Feng], Li, B.[Bin], Huang, J.W.[Ji-Wu],
Detecting facial manipulated videos based on set convolutional neural networks,
JVCIR(77), 2021, pp. 103119.
Elsevier DOI 2106
Digital video forensics, Deepfake, Set convolutional neural network, Set reduce BibRef

Shang, Z.H.[Zhi-Hua], Xie, H.T.[Hong-Tao], Zha, Z.J.[Zheng-Jun], Yu, L.Y.[Ling-Yun], Li, Y.[Yan], Zhang, Y.D.[Yong-Dong],
PRRNet: Pixel-Region relation network for face forgery detection,
PR(116), 2021, pp. 107950.
Elsevier DOI 2106
Face forgery detection, Forgery localization, Inconsistency detection, Relation learning BibRef

Alharbi, A.[Ahmed], Dong, H.[Hai], Yi, X.[Xun], Tari, Z.[Zahir], Khalil, I.[Ibrahim],
Social Media Identity Deception Detection: A Survey,
Surveys(54), No. 3, April 2021, pp. xx-yy.
DOI Link 2106
Identity deception, social botnet, Sybil, sockpuppet, identity theft, identity cloning, detection techniques, fake profile BibRef

Ferrara, M.[Matteo], Franco, A.[Annalisa], Maltoni, D.[Davide],
Face morphing detection in the presence of printing/scanning and heterogeneous image sources,
IET-Bio(10), No. 3, 2021, pp. 290-303.
DOI Link 2106
BibRef


Tinsley, P.[Patrick], Czajka, A.[Adam], Flynn, P.[Patrick],
This Face Does Not Exist... But It Might Be Yours! Identity Leakage in Generative Models,
WACV21(1319-1327)
IEEE DOI 2106
Training, Image resolution, Databases, Training data, Generative adversarial networks, Data models, Faces BibRef

Trinh, L.[Loc], Tsang, M.[Michael], Rambhatla, S.[Sirisha], Liu, Y.[Yan],
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes,
WACV21(1972-1982)
IEEE DOI 2106
Visualization, Prototypes, Detectors, Forgery, Faces BibRef

Hussain, S.[Shehzeen], Neekhara, P.[Paarth], Jere, M.[Malhar], Koushanfar, F.[Farinaz], McAuley, J.[Julian],
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples,
WACV21(3347-3356)
IEEE DOI 2106
Industries, Perturbation methods, Pipelines, Neural networks, Detectors, Media, Video compression BibRef

Yang, S.H.[Shu-Hui], Xue, H.[Han], Ling, J.[Jun], Song, L.[Li], Xie, R.[Rong],
Deep Face Swapping via Cross-identity Adversarial Training,
MMMod21(II:74-86).
Springer DOI 2106
BibRef

Zhang, Z.[Zhewei], Mal, C.[Can], Ding, B.[Bowen], Gao, M.[Meilin],
Detecting Manipulated Facial Videos: A Time Series Solution,
ICPR21(2817-2823)
IEEE DOI 2105
Deep learning, Correlation, Face recognition, Time series analysis, Memory architecture, Physiology, Complexity theory, pattern recognization BibRef

Bonettini, N.[Nicolò], Cannas, E.D.[Edoardo Daniele], Mandelli, S.[Sara], Bondi, L.[Luca], Bestagini, P.[Paolo], Tubaro, S.[Stefano],
Video Face Manipulation Detection Through Ensemble of CNNs,
ICPR21(5012-5019)
IEEE DOI 2105
Training, Computational modeling, Veins, Video sequences, Tools, Feature extraction, Data models, deepfake, video forensics, attention BibRef

Li, M.[Meng], Liu, B.B.[Bei-Bei], Hu, Y.J.[Yong-Jian], Wang, Y.[Yufei],
Exposing Deepfake Videos by Tracking Eye Movements,
ICPR21(5184-5189)
IEEE DOI 2105
Support vector machines, Tracking, Neural networks, Media, Tools, Feature extraction, Physiology BibRef

Liu, Y.C.[Yu-Cheng], Chang, C.M.[Chia-Ming], Chen, I.H.[I-Hsuan], Ku, Y.R.[Yu-Ru], Chen, J.C.[Jun-Cheng],
An Experimental Evaluation of Recent Face Recognition Losses for Deepfake Detection,
ICPR21(9827-9834)
IEEE DOI 2105
Face recognition, Network architecture, Data models, Task analysis, Faces, Videos, Information integrity BibRef

Masi, I.[Iacopo], Killekar, A.[Aditya], Mascarenhas, R.M.[Royston Marian], Gurudatt, S.P.[Shenoy Pratik], Abd Almageed, W.[Wael],
Two-branch Recurrent Network for Isolating Deepfakes in Videos,
ECCV20(VII:667-684).
Springer DOI 2011
BibRef

Gu, Y.W.[Ye-Wei], Zhao, X.F.[Xian-Feng], Gong, C.[Chen], Yi, X.W.[Xiao-Wei],
Deepfake Video Detection Using Audio-visual Consistency,
IWDW20(168-180).
Springer DOI 2103
BibRef

Schütz, M.[Mina], Schindler, A.[Alexander], Siegel, M.[Melanie], Nazemi, K.[Kawa],
Automatic Fake News Detection with Pre-trained Transformer Models,
RISS20(627-641).
Springer DOI 2103
BibRef

Tolosana, R.[Ruben], Romero-Tapiador, S.[Sergio], Fierrez, J.[Julian], Vera-Rodriguez, R.[Ruben],
Deepfakes Evolution: Analysis of Facial Regions and Fake Detection Performance,
IWBDAF20(442-456).
Springer DOI 2103
BibRef

Mishra, R.,
Fake News Detection using Higher-order User to User Mutual-attention Progression in Propagation Paths,
WMF20(2775-2783)
IEEE DOI 2008
Feature extraction, Twitter, Linear programming, Computer vision, Linguistics, Time series analysis BibRef

Chai, L.[Lucy], Bau, D.[David], Lim, S.N.[Ser-Nam], Isola, P.[Phillip],
What Makes Fake Images Detectable? Understanding Properties that Generalize,
ECCV20(XXVI:103-120).
Springer DOI 2011
BibRef

Zhou, J., Pun, C.M., Tong, Y.,
News Image Steganography: A Novel Architecture Facilitates the Fake News Identification,
VCIP20(235-238)
IEEE DOI 2102
Decoding, Training, Perturbation methods, Forgery, Printers, Media, image steganography, information hiding, fake news identification BibRef

Choi, D.H., Lee, H.J., Lee, S., Kim, J.U., Ro, Y.M.,
Fake Video Detection With Certainty-Based Attention Network,
ICIP20(823-827)
IEEE DOI 2011
DeepFake video detection, predictive uncertainty, certainty-key frame, certainty-based attention BibRef

Fernandes, S., Raj, S., Ewetz, R., Pannu, J.S., Jha, S.K.[S. Kumar], Ortiz, E., Vintila, I., Salter, M.,
Detecting Deepfake Videos using Attribution-Based Confidence Metric,
CVPM20(1250-1259)
IEEE DOI 2008
Videos, Face, Measurement, Computational modeling, Training data, Machine learning, Databases BibRef

Tursman, E., George, M., Kamara, S., Tompkin, J.,
Towards Untrusted Social Video Verification to Combat Deepfakes via Face Geometry Consistency,
WMF20(2784-2793)
IEEE DOI 2008
Face, Streaming media, Principal component analysis, Cameras, Mouth, Covariance matrices, Geometry BibRef

Khalid, H., Woo, S.S.,
OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder,
WMF20(2794-2803)
IEEE DOI 2008
Face, Training, Image reconstruction, Forensics, Streaming media, Anomaly detection, Benchmark testing BibRef

Carlini, N., Farid, H.,
Evading Deepfake-Image Detectors with White- and Black-Box Attacks,
WMF20(2804-2813)
IEEE DOI 2008
Forensics, Generators, Perturbation methods, Training, Twitter, Robustness, Optimization BibRef

Agarwal, S., Farid, H., Fried, O., Agrawala, M.,
Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches,
WMF20(2814-2822)
IEEE DOI 2008
Videos, Lips, Face, Shape, Teeth, Robustness BibRef

Guarnera, L., Giudice, O., Battiato, S.,
DeepFake Detection by Analyzing Convolutional Traces,
WMF20(2841-2850)
IEEE DOI 2008
Generative adversarial networks, Videos, Computer architecture, Data models, Convolution, Feature extraction BibRef

Montserrat, D.M., Hao, H., Yarlagadda, S.K., Baireddy, S., Shao, R., Horváth, J., Bartusiak, E., Yang, J., Güera, D., Zhu, F., Delp, E.J.,
Deepfakes Detection with Automatic Face Weighting,
WMF20(2851-2859)
IEEE DOI 2008
Face, Feature extraction, Streaming media, Recurrent neural networks, Training, Tools, Social network services BibRef

Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.,
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics,
CVPR20(3204-3213)
IEEE DOI 2008
Videos, Visualization, Image color analysis, Decoding, YouTube, Training, Detection algorithms BibRef

Wang, Y., Dantcheva, A.,
A video is worth more than 1000 lies. Comparing 3DCNN approaches for detecting deepfakes,
FG20(515-519)
IEEE DOI 2102
Videos, Information integrity, Training, Face recognition, Solid modeling, Faces BibRef

Tong, X., Wang, L., Pan, X., Wang, J.G.,
An Overview of Deepfake: The Sword of Damocles in AI,
CVIDL20(265-273)
IEEE DOI 2102
face recognition, feature extraction, image classification, video signal processing, confrontation detection, Variational Auto-Encoder BibRef

Yeh, C., Chen, H., Tsai, S., Wang, S.,
Disrupting Image-Translation-Based DeepFake Algorithms with Adversarial Attacks,
WACVWS20(53-62)
IEEE DOI 2006
Generators, Mathematical model, Hair, Software algorithms, Software, Machine learning BibRef

Matern, F., Riess, C., Stamminger, M.,
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations,
IVF19(83-92)
IEEE DOI 1902
Face, Visualization, Lighting, Estimation, Geometry BibRef

Jeon, H., Bang, Y., Woo, S.S.,
FakeTalkerDetect: Effective and Practical Realistic Neural Talking Head Detection with a Highly Unbalanced Dataset,
HBU19(1285-1287)
IEEE DOI 2004
face recognition, learning (artificial intelligence), neural nets, video signal processing, FakeTalkerDetect, few shot learning BibRef

He, P., Li, H., Wang, H.,
Detection of Fake Images Via The Ensemble of Deep Representations from Multi Color Spaces,
ICIP19(2299-2303)
IEEE DOI 1910
generative adversarial network, fake image detection, multi color spaces, random forest BibRef

Huh, M.Y.[Min-Young], Liu, A.[Andrew], Owens, A.[Andrew], Efros, A.A.[Alexei A.],
Fighting Fake News: Image Splice Detection via Learned Self-Consistency,
ECCV18(XI: 106-124).
Springer DOI 1810
BibRef

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Emotions in Face Animation, Video Face Synthesis .


Last update:Jul 28, 2021 at 22:23:09