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Balancing Image Quality and Attack Effectiveness in Multi-Objective Adversarial Image Generation

https://doi.org/10.2139/ssrn.5457015
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Fooling Rate and Perceptual Similarity: A Study on the Effectiveness and Quality of DCGAN-based Adversarial Attacks
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Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
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Adversarial attacks on GAN-based image fusion
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On adversarial attack detection in the artificial intelligence era: Fundamentals, a taxonomy, and a review
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SuperstarGAN: Generative adversarial networks for image-to-image translation in large-scale domains
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A survey on adversarial attacks and defences
inferredcites →https://doi.org/10.1109/access.2024.3487479openalex-snapshot
Evaluating Impact of Image Transformations on Adversarial Examples
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DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.17openalex-snapshot
Universal Adversarial Perturbations
inferredcites →https://doi.org/10.1109/cvpr.2018.00068openalex-snapshot
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
inferredcites →https://doi.org/10.1109/cvpr.2018.00084openalex-snapshot
NAG: Network for Adversary Generation
inferredcites →https://doi.org/10.1109/cvpr.2018.00465openalex-snapshot
Generative Adversarial Perturbations
inferredcites →https://doi.org/10.1109/cvpr.2018.00957openalex-snapshot
Boosting Adversarial Attacks with Momentum
inferredcites →https://doi.org/10.1109/iccv51070.2023.00409openalex-snapshot
Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation
inferredcites →https://doi.org/10.1109/iccv51070.2023.00439openalex-snapshot
TRM-UAP: Enhancing the Transferability of Data-Free Universal Adversarial Perturbation via Truncated Ratio Maximization
inferredcites →https://doi.org/10.1109/lcomm.2024.3503721openalex-snapshot
Secure Indoor Localization Against Adversarial Attacks Using DCGAN
inferredcites →https://doi.org/10.1109/msec.2018.2888775openalex-snapshot
Privacy-Preserving Machine Learning: Threats and Solutions
inferredcites →https://doi.org/10.1109/msp.2017.2765202openalex-snapshot
Generative Adversarial Networks: An Overview
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/tpami.2018.2861800openalex-snapshot
Generalizable Data-Free Objective for Crafting Universal Adversarial Perturbations
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3459637.3482029openalex-snapshot
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications
inferredcites →https://doi.org/10.1155/2022/9005552openalex-snapshot
Application of an Improved DCGAN for Image Generation
inferredcites →https://doi.org/10.18034/ajase.v8i1.9openalex-snapshot
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
inferredcites →https://doi.org/10.24963/ijcai.2018/543openalex-snapshot
Generating Adversarial Examples with Adversarial Networks
inferredcites →https://doi.org/10.32604/iasc.2021.019727openalex-snapshot
Adversarial Examples Generation Algorithm through DCGAN
inferredcites →https://doi.org/10.48550/arxiv.1312.6199openalex-snapshot
Intriguing properties of neural networks
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1607.08022openalex-snapshot
Instance Normalization: The Missing Ingredient for Fast Stylization
inferredcites →https://doi.org/10.48550/arxiv.2003.06878openalex-snapshot
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks
inferredcites →https://doi.org/10.48550/arxiv.2303.06302openalex-snapshot
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
inferredcites →https://doi.org/10.5244/c.31.30openalex-snapshot
Fast Feature Fool: A data independent approach to universal adversarial perturbations
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