SOTAVerified

Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

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Showing 1551–1600 of 1808 papers

TitleStatusHype
CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator—0
Semantic Image Attack for Visual Model Diagnosis—0
Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm—0
Model Robustness with Text Classification: Semantic-preserving adversarial attacks—0
Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks?—0
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack—0
Can We Rely on AI?—0
SemDiff: Generating Natural Unrestricted Adversarial Examples via Semantic Attributes Optimization in Diffusion Models—0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification—0
Capsule Neural Networks as Noise Stabilizer for Time Series Data—0
Attacking Perceptual Similarity Metrics—0
Attacking Important Pixels for Anchor-free Detectors—0
Certifiably Robust Variational Autoencoders—0
SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images—0
Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation—0
Chain Association-based Attacking and Shielding Natural Language Processing Systems—0
Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems—0
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers—0
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers—0
Attack-Agnostic Adversarial Detection—0
CharBot: A Simple and Effective Method for Evading DGA Classifiers—0
Sequential Attacks on Agents for Long-Term Adversarial Goals—0
A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays—0
Class-Aware Domain Adaptation for Improving Adversarial Robustness—0
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words—0
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey—0
Bidirectional Contrastive Split Learning for Visual Question Answering—0
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation—0
Cloud Adversarial Example Generation for Remote Sensing Image Classification—0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples—0
SHARP: Search-Based Adversarial Attack for Structured Prediction—0
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples—0
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness—0
Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network"—0
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech—0
Compressed models are NOT miniature versions of large models—0
Compressive Sensing Based Adaptive Defence Against Adversarial Images—0
Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification—0
Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces—0
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models—0
A Survey on Physical Adversarial Attacks against Face Recognition Systems—0
Constrained Adversarial Learning for Automated Software Testing: a literature review—0
Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability—0
Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories—0
Content-based Unrestricted Adversarial Attack—0
Context-aware Adversarial Attack on Named Entity Recognition—0
Contextual adversarial attack against aerial detection in the physical world—0
A Survey on Physical Adversarial Attack in Computer Vision—0
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—0
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified