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IWA: Integrated gradient‐based white‐box attacks for fooling deep neural networks

https://doi.org/10.1002/int.22720
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-66399-9_4openalex-snapshot
Adversarial Examples for Malware Detection
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Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network
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Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/cisda.2009.5356528openalex-snapshot
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inferredcites →https://doi.org/10.1109/cscita.2014.6839299openalex-snapshot
Big Data analytics frameworks
inferredcites →https://doi.org/10.1109/cvpr.2016.282openalex-snapshot
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
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TetraTSDF: 3D Human Reconstruction From a Single Image With a Tetrahedral Outer Shell
inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot
The Limitations of Deep Learning in Adversarial Settings
inferredcites →https://doi.org/10.1109/jsyst.2019.2906120openalex-snapshot
Adversarial-Example Attacks Toward Android Malware Detection System
inferredcites →https://doi.org/10.1109/sp.2016.41openalex-snapshot
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/spw.2018.00009openalex-snapshot
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
inferredcites →https://doi.org/10.1109/tevc.2019.2890858openalex-snapshot
One Pixel Attack for Fooling Deep Neural Networks
inferredcites →https://doi.org/10.1109/tnnls.2018.2886017openalex-snapshot
Adversarial Examples: Attacks and Defenses for Deep Learning
inferredcites →https://doi.org/10.1137/1.9781611974997openalex-snapshot
First-Order Methods in Optimization
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ZOO
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inferredcites →https://doi.org/10.48550/arxiv.1312.6199openalex-snapshot
Intriguing properties of neural networks
inferredcites →https://doi.org/10.48550/arxiv.1605.07277openalex-snapshot
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
inferredcites →https://doi.org/10.48550/arxiv.1802.00420openalex-snapshot
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.2003.01690openalex-snapshot
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
inferredcites →https://doi.org/10.52202/085713openalex-snapshot
Advances in Neural Information Processing Systems 19
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-snapshot
Learning Multiple Layers of Features from Tiny Images

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Cross‐Method Explanation Stability Under Prediction‐Preserving Perturbations in Explainable AI
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Label specificity attack: Change your label as I want
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An improved stochastic gradient descent algorithm based on Rényi differential privacy
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Active forgetting via influence estimation for neural networks
inferred← citeshttps://doi.org/10.1002/int.22997openalex-snapshot
An effective and practical gradient inversion attack
inferred← citeshttps://doi.org/10.1007/978-3-030-85030-2_33openalex-snapshot
Deep Interpretation with Sign Separated and Contribution Recognized Decomposition
inferred← citeshttps://doi.org/10.1016/j.asoc.2024.111969openalex-snapshot
Generation of black-box adversarial attacks using many independent objective-based algorithm for testing the robustness of deep neural networks
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A comprehensive review of explainable AI in cybersecurity: Decoding the black box
inferred← citeshttps://doi.org/10.1016/j.ins.2022.07.157openalex-snapshot
DI-AA: An interpretable white-box attack for fooling deep neural networks
inferred← citeshttps://doi.org/10.1016/j.jisa.2022.103227openalex-snapshot
AB-FGSM: AdaBelief optimizer and FGSM-based approach to generate adversarial examples
inferred← citeshttps://doi.org/10.1016/j.jisa.2025.104172openalex-snapshot
Decision attribution and local extremum-guided black-box adversarial attack with adjustable sparsity and discreteness
inferred← citeshttps://doi.org/10.1016/j.neucom.2025.131503openalex-snapshot
Attacking all tasks at once using adversarial examples in multi-task learning
inferred← citeshttps://doi.org/10.1016/j.neucom.2025.131848openalex-snapshot
Selective black-box adversarial examples: Protecting a specific model while inducing misperceptions in others
inferred← citeshttps://doi.org/10.1038/s41598-025-04691-0openalex-snapshot
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Comrade‐Secure Adversarial Noise for 3D Point Cloud Classification Model
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Edge Learning for 6G-Enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
inferred← citeshttps://doi.org/10.1109/ism59092.2023.00040openalex-snapshot
Towards Imperceptible Adversarial Image Generation: Minimizing Perceptual Difference
inferred← citeshttps://doi.org/10.1109/tbiom.2026.3669723openalex-snapshot
Beyond Global Perturbations: Focused Adversarial Attacks for Face Deepfake
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Image-Scaling Attack on Image Signal Processing Pipelines in Deep Neural Networks-Based Outdoor Vision Applications
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PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch
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The Noise Blowing-Up Strategy Creates High Quality High Resolution Adversarial Images against Convolutional Neural Networks
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Adversarial Attacks on Machine Learning Models for Network Traffic Filtering
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