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 851–900 of 1808 papers

TitleStatusHype
Generating Valid and Natural Adversarial Examples with Large Language Models—0
Generating Watermarked Adversarial Texts—0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—0
Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification—0
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator—0
Defending Against Adversarial Examples by Regularized Deep Embedding—0
Global Robustness Verification Networks—0
Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification—0
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training—0
Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations—0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense—0
Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing—0
GradMDM: Adversarial Attack on Dynamic Networks—0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training—0
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents—0
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems—0
Deep-RBF Networks Revisited: Robust Classification with Rejection—0
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
An Adversarial Approach to Evaluating the Robustness of Event Identification Models—0
Improving Network Interpretability via Explanation Consistency Evaluation—0
Bias Field Poses a Threat to DNN-based X-Ray Recognition—0
Deep Learning for Robust and Explainable Models in Computer Vision—0
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment—0
Harmonic Adversarial Attack Method—0
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems—0
Biologically inspired protection of deep networks from adversarial attacks—0
Adversarial Attacks on Traffic Sign Recognition: A Survey—0
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation—0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning—0
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds—0
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks—0
Adversarial Attack Against Images Classification based on Generative Adversarial Networks—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method—0
HGAttack: Transferable Heterogeneous Graph Adversarial Attack—0
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder—0
Hiding Backdoors within Event Sequence Data via Poisoning Attacks—0
Improving Neural Network Robustness through Neighborhood Preserving Layers—0
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems—0
Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Intermediate Feature Distance—0
Hijacking Vision-and-Language Navigation Agents with Adversarial Environmental Attacks—0
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems—0
Enhancing Transferability of Adversarial Examples with Spatial Momentum—0
An Actor-Critic Method for Simulation-Based Optimization—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