Data to 14 Oct 2026

Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser

arXiv 1712.02976 ↗First posted 8 Dec 2017cs.CVcited by 329

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Abstract (arXiv)

Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification. Standard denoiser suffers from the error amplification effect, in which small residual adversarial noise is progressively amplified and leads to wrong classifications. HGD overcomes this problem by using a loss function defined as the difference between the target model's outputs activated by the clean image and denoised image. Compared with ensemble adversarial training which is the state-of-the-art defending method on large images, HGD has three advantages. First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks. Second, HGD can be trained on a small subset of the images and generalizes well to other images and unseen classes. Third, HGD can be transferred to defend models other than the one guiding it. In NIPS competition on defense against adversarial attacks, our HGD solution won the first place and outperformed other models by a large margin.

Key facts. Cited by 329 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Dec 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 86; 2022: 64; 2023: 66; 2024: 53; 2025: 46; 2026: 14.

329 papers.

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2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 279
2021Enhancing the Transferability of Adversarial Attacks through Variance Tuningcited by 244
2021Admix: Enhancing the Transferability of Adversarial Attackscited by 156
2021On the Convergence and Robustness of Adversarial Trainingcited by 130
2022Frequency Domain Model Augmentation for Adversarial Attackcited by 130
2022Adversarial Texture for Fooling Person Detectors in the Physical Worldcited by 96
2023Diffusion Models for Imperceptible and Transferable Adversarial Attackcited by 94
2022Improving Adversarial Transferability via Neuron Attribution-Based Attackscited by 93
2023Rethinking Model Ensemble in Transfer-based Adversarial Attackscited by 89
2023A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinkingcited by 85
2021Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferabilitycited by 84
2021RobustART: Benchmarking Robustness on Architecture Design and Training Techniquescited by 79

38 more

2023Robust Classification via a Single Diffusion Modelcited by 79
2021Improving Adversarial Robustness via Channel-wise Activation Suppressingcited by 76
2023Content-based Unrestricted Adversarial Attackcited by 73
2023How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defensescited by 66
2023Boosting Adversarial Transferability by Block Shuffle and Rotationcited by 64
2023Structure Invariant Transformation for better Adversarial Transferabilitycited by 62
2021Relating Adversarially Robust Generalization to Flat Minimacited by 57
2022Robust Semantic Communications with Masked VQ-VAE Enabled Codebookcited by 56
2021Exploring Memorization in Adversarial Trainingcited by 55
2021Meta Gradient Adversarial Attackcited by 55
2021Boosting Adversarial Transferability through Enhanced Momentumcited by 54
2021Adversarial Example Detection for DNN Models: A Review and Experimental Comparisoncited by 52
2022ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpointscited by 48
2023Boosting Adversarial Transferability by Achieving Flat Local Maximacited by 48
2021Online Adversarial Purification based on Self-Supervisioncited by 46
2023AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Modelscited by 46
2023Adversarial Prompt Tuning for Vision-Language Modelscited by 44
2021A Comprehensive Evaluation Framework for Deep Model Robustnesscited by 42
2021Consistency Regularization for Adversarial Robustnesscited by 42
2021An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defencescited by 42
2023An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferabilitycited by 42
2021Subspace Adversarial Trainingcited by 41
2022Sparsity Winning Twice: Better Robust Generalization from More Efficient Trainingcited by 41
2021Boosting Fast Adversarial Training with Learnable Adversarial Initializationcited by 40
2022Self-Ensemble Adversarial Training for Improved Robustnesscited by 38
2022Natural Color Fool: Towards Boosting Black-box Unrestricted Attackscited by 38
2021Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fittingcited by 37
2023Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Featurescited by 37
2021Imperceptible Transfer Attack and Defense on 3D Point Cloud Classificationcited by 36
2023Improving the Transferability of Adversarial Samples by Path-Augmented Methodcited by 36
2022Transferable Adversarial Attack based on Integrated Gradientscited by 35
2023Diversifying the High-level Features for better Adversarial Transferabilitycited by 34
2021Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defensescited by 30
2022Towards Efficient Adversarial Training on Vision Transformerscited by 30
2021Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domaincited by 29
2021WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classificationcited by 29
2022Boosting the Transferability of Adversarial Attacks with Global Momentum Initializationcited by 29
2021Class-Aware Robust Adversarial Training for Object Detectioncited by 27

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