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Adversarial Machine Learning at Scale
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Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model's parameters. Adversarial training is the process of explicitly training a model on adversarial examples, in order to make it more robust to attack or to reduce its test error on clean inputs. So far, adversarial training has primarily been applied to small problems. In this research, we apply adversarial training to ImageNet. Our contributions include: (1) recommendations for how to succesfully scale adversarial training to large models and datasets, (2) the observation that adversarial training confers robustness to single-step attack methods, (3) the finding that multi-step attack methods are somewhat less transferable than single-step attack methods, so single-step attacks are the best for mounting black-box attacks, and (4) resolution of a "label leaking" effect that causes adversarially trained models to perform better on adversarial examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process.
Forward citations
Cited by 19 Pith papers
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Anti-Hyperspectral Anomaly Detection: A First Study on Stealthy Lipschitz-Forcing Perturbations Against Unknown Detectors
Develops the first AHAD method using ARAB regularization and Lipschitz-forcing perturbations to produce one energy-efficient signal that evades multiple unknown benchmark HAD detectors.
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Revisiting Model Inversion Evaluation: From Misleading Standards to Reliable Privacy Assessment
Standard model inversion evaluation counts many adversarial false positives as successes; MLLM-based evaluation reveals consistently high false-positive rates across 27 attack setups.
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Hard-Label Black-Box Attacks on 3D Point Clouds
A spectrum-aware decision boundary algorithm enables effective hard-label black-box adversarial attacks on 3D point cloud models by fusing spectral information across classes and performing curvature-aware iterative o...
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MirrorCheck: Efficient Adversarial Defense for Vision-Language Models
MirrorCheck detects adversarial attacks on VLMs via T2I regeneration for semantic consistency checks, using stochastic model selection and one-time perturbations for robustness against adaptive attacks.
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When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift
Cross-AUC averages per-domain AUCs with a polarization term from Wasserstein distance on score distributions to assess deepfake detector generalization under domain shift more realistically than isolated AUC.
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MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defense
MorphStrata generates heterogeneous student models via layer-specific perturbations in a Transformer-based Morphence MTD setup, reporting RMSE gains up to 24% and 98% on AEP data under FGSM and BIM attacks with under ...
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Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models
Random quantum circuits used as adversarial training data reduce successful attack rates on QML models for CIFAR-10 from 89.8% to 68.45% and for CINIC-10 from 94.23% to 78.68%.
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Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models
A meta-prompt and hierarchical detection framework automates LLM red-teaming, achieving 3.9 times higher vulnerability discovery rate than manual methods with 89% accuracy on GPT-OSS-20B.
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Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
Orthogonal subspace decomposition via SVD on vision foundation model features preserves high-rank pre-trained knowledge by freezing principal components and adapting residuals, reducing overfitting for better generali...
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Fooling a Real Car with Adversarial Traffic Signs
A reproducible pipeline produces physical adversarial traffic signs that successfully attack production-grade traffic sign recognition systems in a real car under black-box conditions.
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UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems
UniAda introduces a white-box multi-objective attack using adaptive weighting to generate perturbations that jointly affect steering and speed in E2E ADS, outperforming benchmarks with average deviations of 3.54-29 de...
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Latent Adversarial Defence with Boundary-guided Generation
LAD generates diverse adversarial examples in latent space by perturbing along normals to an SVM-defined decision boundary and uses them for adversarial training to improve DNN robustness.
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Why Blocking Targeted Adversarial Perturbations Impairs the Ability to Learn
Defensive distillation blocks non-targeted adversarial attacks but cannot block targeted ones without preventing the network from learning via its input gradient.
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Learning to Cope with Adversarial Attacks
MLAH agent in deep RL demonstrates hierarchical coping mechanisms and improved reward maintenance under spaced adversarial attacks, at the expense of stability.
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Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids
A statistical pseudo-feature padding layer is added to DNN inputs to defend against false data injection attacks on power grid state estimation.
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Beyond Attack Success Rate: A Multi-Metric Evaluation of Adversarial Transferability in Medical Imaging Models
Perceptual quality metrics correlate strongly with each other but show minimal correlation with attack success rate across medical imaging models and datasets, making ASR alone inadequate for assessing adversarial robustness.
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Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization
The method reaches 84.46% Dice score on brain MR segmentation of gray matter, white matter and major regions using only seven training subjects via adversarial defense and hierarchical task reorganization.
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GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection
GuardNet ensemble of BiLSTMs reaches AUROC 0.747 on blind n=200 test and F1 0.92 on proprietary n=50 set with 50 ms CPU latency for PI/JB detection.
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Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
A survey of quantum adversarial machine learning covering attacks, countermeasures, theoretical underpinnings, trends, and challenges.
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