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 451–500 of 1808 papers

TitleStatusHype
Black-box Adversarial ML Attack on Modulation Classification—0
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution—0
Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation—0
Black-box Targeted Adversarial Attack on Segment Anything (SAM)—0
Automated Decision-based Adversarial Attacks—0
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples—0
AutoAugment Input Transformation for Highly Transferable Targeted Attacks—0
Blurring Fools the Network -- Adversarial Attacks by Feature Peak Suppression and Gaussian Blurring—0
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey—0
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition—0
Boosting Adversarial Transferability of MLP-Mixer—0
Boosting Adversarial Transferability through Enhanced Momentum—0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack—0
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification—0
Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality—0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective—0
Augmented Adversarial Trigger Learning—0
Boosting Black-Box Adversarial Attacks with Meta Learning—0
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors—0
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection—0
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation—0
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References—0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium—0
Audio Adversarial Examples: Attacks Using Vocal Masks—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Attribution for Enhanced Explanation with Transferable Adversarial eXploration—0
Bregman Linearized Augmented Lagrangian Method for Nonconvex Constrained Stochastic Zeroth-order Optimization—0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
Adversarial Attack on Facial Recognition using Visible Light—0
Attribution-driven Causal Analysis for Detection of Adversarial Examples—0
Attribute-Guided Encryption with Facial Texture Masking—0
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools—0
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack—0
Btech thesis report on adversarial attack detection and purification of adverserially attacked images—0
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries—0
Adversarial Attacks and Defenses: An Interpretation Perspective—0
CAAD 2018: Iterative Ensemble Adversarial Attack—0
CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator—0
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification—0
Attention, Please! Adversarial Defense via Activation Rectification and Preservation—0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network—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
Adversarially Robust Neural Architectures—0
Adversarial Attack on Deep Product Quantization Network for Image Retrieval—0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification—0
Capsule Neural Networks as Noise Stabilizer for Time Series Data—0
DA^3: A Distribution-Aware Adversarial Attack against Language Models—0
Darknet Traffic Classification and Adversarial Attacks—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