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Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces [article]

Chaofei Yang, Lei Ding, Yiran Chen, Hai Li
2020 arXiv   pre-print
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

Ravi Chauhan, Ulya Sabeel, Alireza Izaddoost, Shahram Shah Heydari
2021 Journal of Cybersecurity and Privacy  
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  
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]

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.  ...  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

Pengpeng Yang, Daniele Baracchi, Rongrong Ni, Yao Zhao, Fabrizio Argenti, Alessandro Piva
2020 Journal of Imaging  
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]

Izzat Alsmadi
2021 arXiv   pre-print
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]

Yashar Deldjoo and Tommaso Di Noia and Felice Antonio Merra
2020 arXiv   pre-print
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

Wei Yu, Tao Chang, Xiaoting Guo, Xiaodong Wang, Bo Liu, Yang He
2020 IEEE Access  
For more information, see 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

Wei Chen, Weiping Wang, Li Liu, Michael S. Lew
2020 Neurocomputing  
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

Izzat Alsmadi, Nura Aljaafari, Mahmoud Nazzal, Shadan Alhamed, Ahmad H. Sawalmeh, Conrado P. Vizcarra, Abdallah Khreishah, Muhammad Anan, Abdulelah Algosaibi, Mohammed Abdulaziz Al-Naeem, Adel Aldalbahi, Abdulaziz Al-Humam
2022 IEEE Access  
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]

Xin Dong, Hongxu Yin, Jose M. Alvarez, Jan Kautz, Pavlo Molchanov
2021 arXiv   pre-print
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]

Luke A. Bauer, Vincent Bindschaedler
2021 arXiv   pre-print
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]

Xiaobin Li, Hongxu Jiang, Shuangxi Huang, Fangzheng Tian
2020 arXiv   pre-print
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

Changhao Chen, Yishu Miao, Chris Xiaoxuan Lu, Linhai Xie, Phil Blunsom, Andrew Markham, Niki Trigoni
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]

Hao Sun, Zhiqun Zhao, Zhihai He
2020 arXiv   pre-print
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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