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Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces
[article]
2020
arXiv
pre-print
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Specifically, we propose to use novel transformation-aware adversarially perturbed faces as a defense against GAN-based Deepfake attacks. ...
We also propose to use an ensemble-based approach to enhance the defense robustness against GAN-based Deepfake variants under the black-box setting. ...
Cyclic loss is evolved from the cycle consistency loss from CycleGAN [31] , which helps to regularize the structured data. ...
arXiv:2006.07421v1
fatcat:gejxwj47q5gnpphocxeqtdkbby
Polymorphic Adversarial Cyberattacks Using WGAN
2021
Journal of Cybersecurity and Privacy
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Generative Adversarial Network (GAN) is a method proven in generating adversarial data in the domain of multimedia processing, text, and voice, and can produce a high volume of test data that is indistinguishable ...
In this paper, we propose a model to generate adversarial attacks using Wasserstein GAN (WGAN). The attack data synthesized using the proposed model can be used to train an IDS. ...
Acknowledgments: The authors acknowledge and thank Deepa Kishore Malani for her contribution to this research.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/jcp1040037
fatcat:6gcv4ae6ibbzjdiszldr4qkxre
Survey on Generative Adversarial Behavior in Artificial Neural Tasks
2022
Iraqi Journal for Computer Science and Mathematics
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Generative Adversarial Networks (GANs) are a unique class that has recently received a lot of interest due to the popularity of deep generative models. ...
While various reviews for GANs in the image processing arena have been undertaken to date, none have focused on the review of GANs in multi-disciplinary domains. ...
[21] created a surface defect-generation adversarial network (SDGAN) that uses D2 adversarial loss and cycle consistency loss to produce industrial defect pictures. ...
doi:10.52866/ijcsm.2022.02.01.009
fatcat:mfqgweniwzc5pl4oony3vzoh2y
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems
[article]
2020
arXiv
pre-print
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class, and (2) adversarial training for generative adversarial networks (GANs) as a first step towards robust image translation networks. ...
Finally, in gray-box scenarios, blurring can mount a successful defense against disruption. We present a spread-spectrum adversarial attack, which evades blur defenses. ...
[4] explore self-adversarial attacks in cycle-consistent image translation networks. ...
arXiv:2003.01279v3
fatcat:wqx3k2mszfdf3gfwngthhreswm
A Survey of Deep Learning-Based Source Image Forensics
2020
Journal of Imaging
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For each of these tasks, we have highlighted advantages and limitations of the methods currently proposed in this promising and rich research field. ...
To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection ...
[111] proposed a Cycle-GAN-based scheme by fusing the adversarial loss, the cycle consistency loss and the low frequency consistency loss. ...
doi:10.3390/jimaging6030009
pmid:34460606
pmcid:PMC8321025
fatcat:sv5pucjdqffexexdwlrxq4jlni
Adversarial Machine Learning in Text Analysis and Generation
[article]
2021
arXiv
pre-print
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The paper summarizes main research trends in the field such as GAN algorithms, models, types of attacks, and defense against those attacks. ...
The research field of adversarial machine learning witnessed a significant interest in the last few years. ...
Defense Against NLP Adversarial Attacks Generating adversarial attacks on text has shown to be more challenging than for images and audios due to their discrete nature. • Dependency parsing, . ...
arXiv:2101.08675v1
fatcat:73b3v35oebefnhzuuuo52jpdtu
A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks
[article]
2020
arXiv
pre-print
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successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. ...
The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another ...
In particular, the former is realized by enforcing a weight-sharing constraint related to variational auto-encoders, i.e., the encoder-generator pair {E , G } and {E , G } and using cycle-consistency ( ...
arXiv:2005.10322v2
fatcat:4wqcluqgnbbwpkicunn42et5te
UGAN: Unified Generative Adversarial Networks For Multidirectional Text Style Transfer
2020
IEEE Access
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For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020 ...
INDEX TERMS Multidirectional text style transfer, generative adversarial networks, unified generative adversarial networks. 55170 This work is licensed under a Creative Commons Attribution 4.0 License. ...
ACKNOWLEDGMENT The authors would like to gratitude anonymous reviewers for their constructive comments. ...
doi:10.1109/access.2020.2980898
fatcat:posppzrmsfc7pbr4b2xzickc2a
New Ideas and Trends in Deep Multimodal Content Understanding: A Review
2020
Neurocomputing
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Finally, we include several promising directions for future research. ...
where monomodal image classifiers such as VGG, ResNet and Inception module are central topics, this paper will examine recent multimodal deep models and structures, including auto-encoders, generative adversarial ...
Besides, cycle-consistency from cycleGAN [175] is introduced for unsupervised image translation where a self-consistency (reconstruction) loss tries to retain the patterns of input data after a cycle ...
doi:10.1016/j.neucom.2020.10.042
fatcat:hyjkj5enozfrvgzxy6avtbmoxu
Adversarial Machine Learning in Text Processing: A Literature Survey
2022
IEEE Access
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INDEX TERMS Adversarial machine learning, generative adversarial networks, GAN, text generation. ...
Literature showed also using conditional GANs to create latent representation for writing types. ...
Thus, they can work as basic components for defense against different adversarial examples. ...
doi:10.1109/access.2022.3146405
fatcat:emahpmjqmnbjpbhptrrtrjlja4
Deep Neural Networks are Surprisingly Reversible: A Baseline for Zero-Shot Inversion
[article]
2021
arXiv
pre-print
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The crux of our method is to inverse the DNN in a divide-and-conquer manner while re-syncing the inverted layers via cycle-consistency guidance with the help of synthesized data. ...
Moreover, inversion of generators in GANs unveils latent code of a given synthesized face image at 128x128px, which can even, in turn, improve defective synthesized images from GANs. ...
We call it cycle consistency-guided inversion. ...
arXiv:2107.06304v1
fatcat:ohamubvcjffxdlbe7sbioxpu2y
Generative Models for Security: Attacks, Defenses, and Opportunities
[article]
2021
arXiv
pre-print
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In particular, we discuss the use of generative models in adversarial machine learning, in helping automate or enhance existing attacks, and as building blocks for defenses in contexts such as intrusion ...
Finally, we discuss new threats due to generative models: the creation of synthetic media such as deepfakes that can be used for disinformation. ...
For instance, a self-driving car could be made to ignore a stop sign [213] when provided with specially crafted malicious inputs. Finally, there exist defenses against these attacks. ...
arXiv:2107.10139v2
fatcat:wjb4dcdpvveztd2h4aretus56a
Robustness-aware 2-bit quantization with real-time performance for neural network
[article]
2020
arXiv
pre-print
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In this paper, a novel robustness-aware 2-bit quantization scheme is proposed for NN base on binary NN and generative adversarial network(GAN), witch improves the performance by enriching the information ...
Meanwhile, the experimental results also demonstrate that the proposed method is robust under the FGSM adversarial samples attack. ...
Acknowledgements We are grateful to anonymous reviewers for their constructive comments. This work is partially supported by the National Science Foundation of China(NSFC) under Grant No. 61872017. ...
arXiv:2010.11271v1
fatcat:obgrlk2pfncyhgf5ekbh4zzzhq
MotionTransformer: Transferring Neural Inertial Tracking between Domains
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Inertial information processing plays a pivotal role in egomotion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. ...
Lilian Zhang at National University of Defense Technology, China for their useful assistance and valuable discussion, who are supported by the National Natural Science Foundation of China (Grants Nos. ...
the learning method for jointly training the modules of our MotionTransformer, including GAN loss L G , reconstruction loss L AE , prediction loss L pred , cycle-consistency L cycle and perceptual consistency ...
doi:10.1609/aaai.v33i01.33018009
fatcat:5u4zvhbcoratljherau3ux2rta
Reciprocal Learning Networks for Human Trajectory Prediction
[article]
2020
arXiv
pre-print
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a new method for network prediction, called reciprocal attack for matched prediction. ...
Based on this constraint, we borrow the concept of adversarial attacks of deep neural networks, which iteratively modifies the input of the network to match the given or forced network output, and develop ...
Cycle consistency learning. ...
arXiv:2004.04340v1
fatcat:bz7awbchnfct5jsbc2za75ucbu
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