Adversarial Defense

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

10 leaderboard tables shown for this task, 10 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

5 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 216 papers with code (403 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Towards Deep Learning Models Resistant to Adversarial Attacks
  19 Jun 2017 59 repositories listed Syntology ran 9 of 17 samples · 8 unverified · 13 pointer-only (licence)
  Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.
• Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
  28 Mar 2019 14 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations.
• Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
  3 Oct 2016 13 repositories listed Syntology ran 3 of 26 samples · 23 unverified · 26 pointer-only (licence)
  An adversarial example library for constructing attacks, building defenses, and benchmarking both
• Certified Adversarial Robustness via Randomized Smoothing
  8 Feb 2019 12 repositories listed
  We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the ℓ₂ norm.
• The Limitations of Deep Learning in Adversarial Settings
  24 Nov 2015 11 repositories listed
  In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between…
• Theoretically Principled Trade-off between Robustness and Accuracy
  24 Jan 2019 9 repositories listed Syntology ran 12 of 15 samples · 3 unverified · 4 pointer-only (licence)
  We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples.
• Adversarial Training for Free!
  29 Apr 2019 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks.
• ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies
  26 Nov 2018 5 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  However, both natural and robust accuracies, in classifying clean and adversarial images, respectively, of the trained robust models are far from satisfactory.
• Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
  17 May 2018 5 repositories listed Syntology ran 2 of 5 samples · 3 unverified · 4 pointer-only (licence)
  Defense-GAN is trained to model the distribution of unperturbed images.
• ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
  14 Aug 2017 5 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating…
• Decoupled Kullback-Leibler Divergence Loss
  23 May 2023 4 repositories listed Syntology ran 7 of 11 samples · 4 unverified · 4 pointer-only (licence)
  In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists of 1) a weighted Mean…
• On Evaluating Adversarial Robustness
  18 Feb 2019 4 repositories listed
  Correctly evaluating defenses against adversarial examples has proven to be extremely difficult.
• Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
  1 Feb 2018 4 repositories listed
  We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples.
• Certified Defenses against Adversarial Examples
  29 Jan 2018 4 repositories listed
  While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs.
• AOGNets: Compositional Grammatical Architectures for Deep Learning
  15 Nov 2017 4 repositories listed Syntology ran 0 of 8 samples · 8 unverified · 1 pointer-only (licence)
  This paper presents deep compositional grammatical architectures which harness the best of two worlds: grammar models and DNNs.
• AdvFAS: A robust face anti-spoofing framework against adversarial examples
  4 Aug 2023 3 repositories listed
  Ensuring the reliability of face recognition systems against presentation attacks necessitates the deployment of face anti-spoofing techniques.
• Robust Classification via a Single Diffusion Model
  24 May 2023 3 repositories listed Syntology ran 0 of 3 samples · 3 unverified · 3 pointer-only (licence)
  As RDC does not require training on particular adversarial attacks, we demonstrate that it is more generalizable to defend against multiple unseen threats.
• Learnable Boundary Guided Adversarial Training
  23 Nov 2020 3 repositories listed
  Previous adversarial training raises model robustness under the compromise of accuracy on natural data.
• ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense
  2 Jan 2020 3 repositories listed Syntology ran 0 of 8 samples · 8 unverified
  There has been extensive research on developing defense techniques against adversarial attacks; however, they have been mainly designed for specific model families or application domains, therefore, they cannot be…
• Testing Robustness Against Unforeseen Adversaries
  21 Aug 2019 3 repositories listed Syntology ran 3 of 18 samples · 15 unverified · 3 pointer-only (licence)
  To narrow in on this discrepancy between research and reality we introduce ImageNet-UA, a framework for evaluating model robustness against a range of unforeseen adversaries, including eighteen new non-L_p attacks.
• Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
  9 Jun 2019 3 repositories listed Syntology ran 1 of 3 samples · 2 unverified
  In this paper, we employ adversarial training to improve the performance of randomized smoothing.
• Robust Decision Trees Against Adversarial Examples
  27 Feb 2019 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models and how to make tree-based models robust…
• advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
  20 Feb 2019 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  advertorch is a toolbox for adversarial robustness research.
• Sample Correlation for Fingerprinting Deep Face Recognition
  30 Dec 2024 2 repositories listed
  Face recognition has witnessed remarkable advancements in recent years, thanks to the development of deep learning techniques.
• DAD++: Improved Data-free Test Time Adversarial Defense
  10 Sep 2023 2 repositories listed
  With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness has become a crucial concern in the…
• Robustifying Point Cloud Networks by Refocusing
  10 Aug 2023 2 repositories listed Syntology ran 15 of 22 samples · 7 unverified · 22 pointer-only (licence)
  In this study, we develop a general mechanism to increase neural network robustness based on focus analysis.
• SMUG: Towards robust MRI reconstruction by smoothed unrolling
  14 Mar 2023 2 repositories listed
  To address this problem, we propose a novel image reconstruction framework, termed SMOOTHED UNROLLING (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust…
• Visual Prompting for Adversarial Robustness
  12 Oct 2022 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  In this work, we leverage visual prompting (VP) to improve adversarial robustness of a fixed, pre-trained model at testing time.
• LPF-Defense: 3D Adversarial Defense based on Frequency Analysis
  23 Feb 2022 2 repositories listed
  Although 3D point cloud classification has recently been widely deployed in different application scenarios, it is still very vulnerable to adversarial attacks.
• Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization
  23 Dec 2021 2 repositories listed
  We first show that the commonly-used Fast-AT is equivalent to using a stochastic gradient algorithm to solve a linearized BLO problem involving a sign operation.

Syntology lines on 18 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.