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DA-FDFtNet: Dual Attention Fake Detection Fine-tuning Network to Detect Various AI-Generated Fake Images
[article]
2021
arXiv
pre-print
Preserved Fulltext
In this work, we propose Dual Attention Fake Detection Fine-tuning Network (DA-FDFtNet) to detect the manipulated fake face images from the real face data. ...
Due to the advancement of Generative Adversarial Networks (GAN), Autoencoders, and other AI technologies, it has been much easier to create fake images such as "Deepfakes". ...
Keywords:
Fake Image Detection, Neural Networks, Fine-tuning
1. ...
arXiv:2112.12001v1
fatcat:b7wmsotrqndqhd5e7j4cigiray
DeepFake Detection for Human Face Images and Videos: A Survey
2022
IEEE Access
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To identify and classify DeepFakes, research in DeepFake detection using deep neural networks (DNNs) has attracted increased interest. ...
We hope that the knowledge encompassed in this survey will accelerate the use of deep learning in face image and video DeepFake detection methods. ...
(DIP) are introduced to increase the robustness of CNN (ResNet and VGG)-based deep-fake detectors. ...
doi:10.1109/access.2022.3151186
fatcat:imz6hdtofrbxfcfi6kput2mffi
Deep Learning in Information Security
[article]
2018
arXiv
pre-print
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If DL-methods succeed to solve problems on a data type in one domain, they most likely will also succeed on similar data from another domain. ...
Machine learning has a long tradition of helping to solve complex information security problems that are difficult to solve manually. ...
Wang et al. use the representation-learning capabilities of neural networks to increase the performance of Commercial
Off-the-Shelf (COTS) forensic face matchers [163]. ...
arXiv:1809.04332v1
fatcat:xfb7lgrkw5cirdl3qvmg3ssnbi
A Survey of Deep Fake Detection for Trial Courts
[article]
2022
arXiv
pre-print
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A recent surge of generated fake imagery and videos using neural networks is DeepFake. DeepFake algorithms can create fake images and videos that humans cannot distinguish from authentic ones. ...
This paper presents a survey of methods used to detect DeepFakes and datasets available for detecting DeepFakes in the literature to date. ...
Unlike conventional methods, they also proposed to use pretrained models with only a few real image datasets for fine-tuning in siamese networks to efficiently detect the fake images in a highly unbalanced ...
arXiv:2205.15792v1
fatcat:gk2g27qpovevretxt3u23mwhvy
Neuro-Symbolic Learning: Principles and Applications in Ophthalmology
[article]
2022
arXiv
pre-print
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Attempts have been made to overcome the challenges in neural network computing by representing and embedding domain knowledge in terms of symbolic representations. ...
However, challenges such as interpretability, explainability, robustness, safety, trust, and sensibility remain unsolved in neural network technologies, despite the fact that they will unavoidably be addressed ...
There is also a problem of overfitting in deep learning networks which weaken the robustness to adversarial attacks [98] . It is hard to explain the observed overfitting. ...
arXiv:2208.00374v1
fatcat:pktmnomj3bbwpjyj7lmu37rl7i
Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection
[article]
2022
arXiv
pre-print
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Online media data, in the forms of images and videos, are becoming mainstream communication channels. ...
This motivates a growing interest of research in media tampering detection, i.e., using deep learning techniques to examine whether media data have been maliciously manipulated. ...
Given these robust features, the localization network is then trained in an adversarial manner with a PatchGAN discriminator [119] , which improves the localization ability of the generator and the image ...
arXiv:2212.05667v1
fatcat:iuqs2d3qhbay3gryewjlbk75jm
A Survey of Biometric Recognition Using Deep Learning
2020
EAI Endorsed Transactions on Energy Web
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The paper starts with biometric basics, transfer learning in deep biometrics, an overview of convolutional neural networks, and then survey work. ...
Biometrics is a technique used to define, assess, and quantify a person's physical and behavioral property. ...
In section 5, the use of Generative Adversarial Network in biometric recognition is provided. ...
doi:10.4108/eai.27-10-2020.166775
fatcat:aihqgrk6pvbxpa54umpemr4s7a
Comparison of Deepfake Detection Techniques through Deep Learning
2022
Journal of Cybersecurity and Privacy
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Because digital evidence is critical to the outcome of many legal cases, detecting deepfake media is extremely important and in high demand in digital forensics. ...
Deepfakes are being widely used as a malicious source of misinformation in court that seek to sway a court's decision. ...
Acknowledgments: The authors would like to show their gratitude to Shonda Bernadin and the MDPI journal reviewers. ...
doi:10.3390/jcp2010007
fatcat:t7blxnyr65a73nlot4lukg32g4
Countering Malicious DeepFakes: Survey, Battleground, and Horizon
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2103.00218v1 fatcat:dgucz5mitbbh7fxoypryg6eoiiOther Versions
Nevertheless, the overview of such battleground and the new direction is unclear and neglected by recent surveys due to the rapid increase of related publications, limiting the in-depth understanding of ...
To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of DeepFake detection ...
Fernandes et al. (2019) use neural ordinary differential equation (Neural-ODE) (Chen et al., 2018b) trained on the original videos to predict the heart rate of testing videos. ...
arXiv:2103.00218v3
fatcat:ufeslcp23rghhmx474u25acoje
State of the Art on Neural Rendering
[article]
2020
arXiv
pre-print
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Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. ...
With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. ...
Face-Forensics++ [RCV * 19] offers a large-scale dataset of different image synthesis and manipulation methods, suited to train deep neural networks in a supervised fashion. ...
arXiv:2004.03805v1
fatcat:6qs7ddftkfbotdlfd4ks7llovq
Ensembling with Deep Generative Views
[article]
2021
arXiv
pre-print
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artifacts in GAN-generated images. ...
Here, we investigate whether such views can be applied to real images to benefit downstream analysis tasks such as image classification. ...
Deep generative image models using a laplacian sarial networks with limited data. In Adv. Neural Inform.
pyramid of adversarial networks. In Adv. Neural Inform. ...
arXiv:2104.14551v1
fatcat:mhkfsgecxbcu5jbpa33rhme6e4
BIOSIG 2021 - Complete Volume
2021
Biometrics and Electronic Signatures
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The facial similarity measure is determined via a deep convolutional neural network. ...
The proposed network provides a quantitative similarity score for any two given faces and has been applied to large-scale face datasets to identify similar face pairs. ...
The authors would like to thank Dr. Mai Guangcan for his gratitude in offering the reconstructed face images. ...
dblp:conf/biosig/X21
fatcat:susbfwcwi5dljbhxc4xxyv6dnq
Robust Face-Swap Detection Based on 3D Facial Shape Information
[article]
2021
arXiv
pre-print
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Maliciously-manipulated images or videos - so-called deep fakes - especially face-swap images and videos have attracted more and more malicious attackers to discredit some key figures. ...
In this paper, we propose a biometric information based method to fully exploit the appearance and shape feature for face-swap detection of key figures. ...
Some classifica-tion neural networks can be applied to detect deep fakes and achieve satisfied performance. ...
arXiv:2104.13665v1
fatcat:3qa4bkw5enc5pdufiu5rbewpai
Generation of Synthetic Data for Handwritten Word Alteration Detection
2021
IEEE Access
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In [36] , Generative Adversarial Network (GAN) is also successfully used to create synthetic handwritten French and Arabic word images. ...
In this method, neural network generative model is used to create synthetic digit images with sufficient fidelity and diversity. ...
doi:10.1109/access.2021.3059342
fatcat:5xjgzkvskjdhhfqf2txtlljlni
A Survey on 3D-aware Image Synthesis
[article]
2022
arXiv
pre-print
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The task of 3D-aware image synthesis has taken the field of computer vision by storm, with hundreds of papers accepted to top-tier journals and conferences in recent year (mainly the past two years), but ...
This includes 3D-aware generative image synthesis, which produces high-fidelity images in a 3D-consistent manner while simultaneously capturing compact surfaces of objects from pure image collections without ...
In this way, these networks can be pre-trained, and then applied to novel scenes without optimization or additional per-scene fine-tuning. ...
arXiv:2210.14267v2
fatcat:hfs2r62afbcrhe4p5fd2qd3dyu
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