Adversarial Attack

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 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

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

Subtasks archive 2025-07-28

4 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 745 papers with code (1,808 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.
• Towards Evaluating the Robustness of Neural Networks
  16 Aug 2016 27 repositories listed Syntology ran 3 of 13 samples · 10 unverified · 1 pointer-only (licence)
  Defensive distillation is a recently proposed approach that can take an arbitrary neural network, and increase its robustness, reducing the success rate of current attacks' ability to find adversarial examples from 95%…
• Universal and Transferable Adversarial Attacks on Aligned Language Models
  27 Jul 2023 25 repositories listed Syntology ran 33 of 61 samples · 28 unverified · 4 pointer-only (licence)
  Specifically, our approach finds a suffix that, when attached to a wide range of queries for an LLM to produce objectionable content, aims to maximize the probability that the model produces an affirmative response…
• 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
• 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…
• Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
  3 Oct 2019 9 repositories listed Syntology ran 3 of 13 samples · 10 unverified · 2 pointer-only (licence)
  Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.
• 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.
• Deep Variational Information Bottleneck
  1 Dec 2016 9 repositories listed Syntology ran 6 of 11 samples · 5 unverified · 3 pointer-only (licence)
  We present a variational approximation to the information bottleneck of Tishby et al.
• Provable defenses against adversarial examples via the convex outer adversarial polytope
  2 Nov 2017 8 repositories listed Syntology ran 21 of 44 samples · 23 unverified · 2 pointer-only (licence)
  We propose a method to learn deep ReLU-based classifiers that are provably robust against norm-bounded adversarial perturbations on the training data.
• Boosting Adversarial Attacks with Momentum
  17 Oct 2017 7 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also…
• Foolbox: A Python toolbox to benchmark the robustness of machine learning models
  13 Jul 2017 7 repositories listed
  Foolbox is a new Python package to generate such adversarial perturbations and to quantify and compare the robustness of machine learning models.
• Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification
  11 Mar 2021 6 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 1 pointer-only (licence)
  Compared to the typically tightest but very costly semidefinite programming (SDP) based incomplete verifiers, we obtain higher verified accuracy with three orders of magnitudes less verification time.
• 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.
• EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
  13 Sep 2017 6 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Recent studies have highlighted the vulnerability of deep neural networks (DNNs) to adversarial examples - a visually indistinguishable adversarial image can easily be crafted to cause a well-trained model to…
• Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers
  17 Nov 2021 5 repositories listed
  Multi-Object Tracking (MOT) has achieved aggressive progress and derived many excellent deep learning trackers.
• Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems
  3 Mar 2020 5 repositories listed
  This type of manipulated images and video have been coined Deepfakes.
• Real-world adversarial attack on MTCNN face detection system
  14 Oct 2019 5 repositories listed
  Recent studies proved that deep learning approaches achieve remarkable results on face detection task.
• 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.
• Local Gradients Smoothing: Defense against localized adversarial attacks
  3 Jul 2018 5 repositories listed
  Deep neural networks (DNNs) have shown vulnerability to adversarial attacks, i.
• 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…
• Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack
  21 Nov 2022 4 repositories listed
  Via BASAR, we find on-manifold adversarial samples are extremely deceitful and rather common in skeletal motions, in contrast to the common belief that adversarial samples only exist off-manifold.
• Patch-wise Attack for Fooling Deep Neural Network
  14 Jul 2020 4 repositories listed
  By adding human-imperceptible noise to clean images, the resultant adversarial examples can fool other unknown models.
• BERT-ATTACK: Adversarial Attack Against BERT Using BERT
  21 Apr 2020 4 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 1 pointer-only (licence)
  Adversarial attacks for discrete data (such as texts) have been proved significantly more challenging than continuous data (such as images) since it is difficult to generate adversarial samples with gradient-based…
• AdvHat: Real-world adversarial attack on ArcFace Face ID system
  23 Aug 2019 4 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 3 pointer-only (licence)
  In this paper we propose a novel easily reproducible technique to attack the best public Face ID system ArcFace in different shooting conditions.
• On Evaluating Adversarial Robustness
  18 Feb 2019 4 repositories listed
  Correctly evaluating defenses against adversarial examples has proven to be extremely difficult.
• Distributionally Adversarial Attack
  16 Aug 2018 4 repositories listed
  Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of…
• Learn To Pay Attention
  6 Apr 2018 4 repositories listed
  We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification.
• 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.
• Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation
  12 Oct 2022 3 repositories listed Syntology ran 3 of 6 samples · 3 unverified · 4 pointer-only (licence)
  Furthermore, RAP can be naturally combined with many existing black-box attack techniques, to further boost the transferability.

Syntology lines on 17 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.