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Adaptive Black-Box Adversarial Attack Based on Deep Reinforcement Learning Against Deep Learning Models

https://doi.org/10.2139/ssrn.5387355
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DI-AA: An interpretable white-box attack for fooling deep neural networks
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Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution
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Towards Understanding and Improving Adversarial Robustness of Vision Transformers
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Robustness with Query-efficient Adversarial Attack using Reinforcement Learning
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Enhancing the Transferability of Adversarial Attacks with Stealth Preservation
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AutoAttacker: A reinforcement learning approach for black-box adversarial attacks
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The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
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DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model
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Deep Reinforcement Learning for Autonomous Driving: A Survey
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Gradient Correction for White-Box Adversarial Attacks
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Physical Adversarial Attack Meets Computer Vision: A Decade Survey
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Deep Learning Enabled Semantic Communications With Speech Recognition and Synthesis
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SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations
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Hard-label based Small Query Black-box Adversarial Attack
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Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach
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Adversarial Attacks and Countermeasures on Image Classification-based Deep Learning Models in Autonomous Driving Systems: A Systematic Review
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DifAttack: Query-Efficient Black-Box Adversarial Attack via Disentangled Feature Space
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Mutual-Modality Adversarial Attack with Semantic Perturbation
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Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution
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Computation-efficient deep learning for computer vision: A survey
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Adversarial Attack and Defense: A Survey
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Application of Deep Learning-Based Natural Language Processing in Multilingual Sentiment Analysis
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Intriguing properties of neural networks
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Adversarial Attacks and Defences: A Survey
inferredcites →https://doi.org/10.48550/arxiv.1906.06919openalex-snapshot
Improving Black-box Adversarial Attacks with a Transfer-based Prior
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Black-Box Adversarial Attack with Transferable Model-based Embedding
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AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows
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Local Black-box Adversarial Attacks: A Query Efficient Approach
inferredcites →https://doi.org/10.48550/arxiv.2404.00924openalex-snapshot
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks
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Learning Multiple Layers of Features from Tiny Images
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Computer Vision and Deep Learning Transforming Image Recognition and Beyond
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Advances in Neural Information Processing Systems 19
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