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Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems [article]

Nataniel Ruiz, Sarah Adel Bargal, Stan Sclaroff
2020 arXiv   pre-print
class, and (2) adversarial training for generative adversarial networks (GANs) as a first step towards robust image translation networks.  ...  Some systems can also modify targeted attributes such as hair color or age. This type of manipulated images and video have been coined Deepfakes.  ...  manipulation by disrupting conditional image translation facial manipulation networks using adapted adversarial attacks.  ... 
arXiv:2003.01279v3 fatcat:wqx3k2mszfdf3gfwngthhreswm

FakeTagger: Robust Safeguards against DeepFake Dissemination via Provenance Tracking [article]

Run Wang, Felix Juefei-Xu, Meng Luo, Yang Liu, Lina Wang
2021 arXiv   pre-print
Unfortunately, existing studies that propose various approaches, in fighting against DeepFake and determining if the facial image is real or fake, is still at an early stage.  ...  In recent years, DeepFake is becoming a common threat to our society, due to the remarkable progress of generative adversarial networks (GAN) in image synthesis.  ...  [44] focus on the white-box and gray-box settings in DeepFake generation by presenting a spread-spectrum disruption on conditional image translation networks, rather than the simple evaluation on face-swapping  ... 
arXiv:2009.09869v3 fatcat:eemjt2fnxrgr7eg4dursbec5hu

Restricted Black-box Adversarial Attack Against DeepFake Face Swapping [article]

Junhao Dong, Yuan Wang, Jianhuang Lai, Xiaohua Xie
2022 arXiv   pre-print
Specially, we propose the Transferable Cycle Adversary Generative Adversarial Network (TCA-GAN) to construct the adversarial perturbation for disrupting unknown DeepFake systems.  ...  In order to prevent this fraud, some researchers have begun to study the adversarial methods against DeepFake or face manipulation.  ...  to generate powerful adversarial examples against DeepFake systems.  ... 
arXiv:2204.12347v1 fatcat:76rcbkxadfbmpl2j7a3pekl3gi

MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes [article]

Zhikai Chen and Lingxi Xie and Shanmin Pang and Yong He and Bo Zhang
2021 arXiv   pre-print
This paper presents MagDR, a mask-guided detection and reconstruction pipeline for defending deepfakes from adversarial attacks.  ...  Prior works revealed the possibility to disrupt deepfakes by adding adversarial perturbations to the source data, but we argue that the threat has not been eliminated yet.  ...  Among the first to introduce adversarial examples against deep neural networks was [41] . After that, Goodfellow et al.  ... 
arXiv:2103.14211v1 fatcat:5knvvbckobhrtaanq3c6u34jma

The Creation and Detection of Deepfakes: A Survey [article]

Yisroel Mirsky, Wenke Lee
2020 arXiv   pre-print
Since then, these 'deepfakes' have advanced significantly. In this paper, we explore the creation and detection of deepfakes and provide an in-depth view of how these architectures work.  ...  The purpose of this survey is to provide the reader with a deeper understanding of (1) how deepfakes are created and detected, (2) the current trends and advancements in this domain, (3) the shortcomings  ...  To combat deepfakes, the authors of [102] show how adversarial machine learning can be used to disrupt and corrupt deepfake networks. e authors perform adversarial machine learning to add cra ed noise  ... 
arXiv:2004.11138v3 fatcat:xqabyslmdfhyznm7msqp3wznnq

Media Forensics and DeepFakes: an overview [article]

Luisa Verdoliva
2020 arXiv   pre-print
This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos.  ...  So-called deepfakes can be used to manipulate public opinion during elections, commit fraud, discredit or blackmail people. Potential abuses are limited only by human imagination.  ...  Lossy compression is a further intrinsic defence against adversarial attacks [244] .  ... 
arXiv:2001.06564v1 fatcat:b3izh3hcmrae5frbd2bv3tmlgu

A Survey on Deepfake Video Detection

Peipeng Yu, Zhihua Xia, Jianwei Fei, Yujiang Lu
2021 IET Biometrics  
Recently, deepfake videos, generated by deep learning algorithms, have attracted widespread attention. Deepfake technology can be used to perform face manipulation with high realism.  ...  This review aims to demonstrate the current research status of deepfake video detection, especially, generation process, several detection methods and existing benchmarks.  ...  However, due to inherent defects, neural networks cannot resist attacks of adversarial samples [86] [87] [88] .  ... 
doi:10.1049/bme2.12031 fatcat:cdwx3jw6ajczplw7le6jsl5wy4

Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach

Deeraj Nagothu, Ronghua Xu, Yu Chen, Erik Blasch, Alexander Aved
2022 Future Internet  
to deepfake attacks that enable manipulation of audio or video streams to mimic any targeted person.  ...  The proposed signal estimation workflow was deployed on a continuous audio/video input for resilience against frame manipulation attacks.  ...  It is applicable to audio and video authentication and results in a generalized solution against media manipulation attacks.  ... 
doi:10.3390/fi14050125 fatcat:xtjvjjscmbbw3lfstbtyovrkie

Fighting Deepfake by Exposing the Convolutional Traces on Images [article]

Luca Guarnera
2020 arXiv   pre-print
Widespread mobile apps like FACEAPP make use of the most advanced Generative Adversarial Networks (GAN) to produce extreme transformations on human face photos such gender swap, aging, etc.  ...  Advances in Artificial Intelligence and Image Processing are changing the way people interacts with digital images and video.  ...  -Spin-off of University of Catania (https://www.ictlab.srl), which provided domain expertise and computational power that greatly assisted the activity.  ... 
arXiv:2008.04095v1 fatcat:jqdi4p4kjvdljgoh7xdzfr455q

Fighting Deepfake by Exposing the Convolutional Traces on Images

Luca Guarnera, Oliver Giudice, Sebastiano Battiato.
2020 IEEE Access  
The stage changes when surprising results of Deepfake images were obtained by Style Generative Adversarial Network (STYLEGAN) [13] .  ...  [10] , is a method capable of performing image-to-image translations on multiple domains using a single model (e.g, change hair color, facial expression).  ...  CONCLUSIONS AND FUTURE WORKS In this paper, a finalization of a former work on analysis of Deepfake images was presented.  ... 
doi:10.1109/access.2020.3023037 fatcat:no5rh32sazceflsumrzwvqo5am

A New Hermeneutics of Suspicion? The Challenge of deepfakes to Theological Epistemology

Clifford Anderson
2019 Cursor_ Zeitschrift für explorative Theologie  
In this paper, I provide an introduction to deepfakes and related machine-learning technologies for theologians, considering their potential use and misuse in theology.  ...  As we explore the topic, we will find that the phenomenon of deepfakes brings us deep into the theology of mediation, pushing us to ponder the relation between εικών and είδος (icon and idea).  ...  The development of a technique termed "Generative Adversarial Networks" (GANS) reduced the computational expense of producing deepfake videos. 25 The leading idea is to pit two deep learning models against  ... 
doi:10.21428/fb61f6aa.771d30b7 fatcat:dntiks2esngwphrrkhnjnuar4q

Advances in adversarial attacks and defenses in computer vision: A survey [article]

Naveed Akhtar, Ajmal Mian, Navid Kardan, Mubarak Shah
2021 arXiv   pre-print
However, it is now known that DL is vulnerable to adversarial attacks that can manipulate its predictions by introducing visually imperceptible perturbations in images and videos.  ...  In [2], we reviewed the contributions made by the computer vision community in adversarial attacks on deep learning (and their defenses) until the advent of year 2018.  ...  [244] devised a facial landmark manipulation method to mislead recognition systems.  ... 
arXiv:2108.00401v2 fatcat:23gw74oj6bblnpbpeacpg3hq5y

Policies of Artificial Intelligence in the EU: Learning Curve from the UK and China?

Ms. Tanzeela Jameel, Dr. Adam Saud
2022 Journal of European studies  
Based on some recent events, this study also highlights the role of adversarial AI in promoting cybercrimes in the form of phishing and data breach attacks in the EU.  ...  This work sheds light on the impact of adversarial AI from the point of view of governance.  ...  "Adversarial deepfakes: Evaluating vulnerability of deepfake detectors to adversarial examples."  ... 
doi:10.56384/jes.v38i2.252 fatcat:yyl4nqp7ofhwhffrakgdz427ba

Digital Forensics Triage Classification Model using Hybrid Learning Approaches

Mohmed Afridhi L, Palanivel K
2022 International Journal of Innovative Research in Computer Science & Technology  
The Internet and the accessibility of gadgets with connectivity have resulted in the global spread of cyber threats and cybercrime, posing significant hurdles for digital forensics.  ...  Consequently, the volume of information that may need to be investigated is growing, necessitating the development of new forensic technologies and methods.  ...  Automated Detection Most deepfake detection systems, such as Face-Forensics++, depend on Deep Neural Networks (DNN) to locate images and videos.  ... 
doi:10.55524/ijircst.2022.10.3.7 fatcat:jgrkujyugnd7xau62upc65hdyi

A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

Ivan Castillo Castillo Camacho, Kai Wang
2021 Journal of Imaging  
of computer graphics images and detection of emerging Deepfake images.  ...  In this review, we cover a broad range of image forensics problems including the detection of routine image manipulations, detection of intentional image falsifications, camera identification, classification  ...  During recent years, an evolution of disinformation has appeared to manipulate and disrupt public opinion.  ... 
doi:10.3390/jimaging7040069 pmid:34460519 pmcid:PMC8321383 fatcat:72zd7nyaifhvlgcxv22zztpm4y
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