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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1151–1200 of 1808 papers

TitleStatusHype
A Perceptual Distortion Reduction Framework: Towards Generating Adversarial Examples with High Perceptual Quality and Attack Success Rate—0
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection—0
AdvHaze: Adversarial Haze Attack—0
Delving into Data: Effectively Substitute Training for Black-box Attack—0
3D Adversarial Attacks Beyond Point CloudCode1
Influence Based Defense Against Data Poisoning Attacks in Online Learning—0
Learning Transferable 3D Adversarial Cloaks for Deep Trained DetectorsCode0
Towards Adversarial Patch Analysis and Certified Defense against Crowd CountingCode0
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions—0
Robust Certification for Laplace Learning on Geometric Graphs—0
Staircase Sign Method for Boosting Adversarial AttacksCode1
Adversarial Diffusion Attacks on Graph-based Traffic Prediction ModelsCode0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
R&R: Metric-guided Adversarial Sentence GenerationCode1
Fashion-Guided Adversarial Attack on Person SegmentationCode0
Mitigating Adversarial Attack for Compute-in-Memory Accelerator Utilizing On-chip Finetune—0
Distributed Estimation over Directed Graphs Resilient to Sensor Spoofing—0
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic NetworkCode0
Semantically Stealthy Adversarial Attacks against Segmentation Models—0
Evaluating Neural Model Robustness for Machine Comprehension—0
Statistical inference for individual fairnessCode0
Robust Reinforcement Learning under model misspecificationCode0
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object TrackingCode1
Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond—0
Vulnerability of Appearance-based Gaze Estimation—0
Grey-box Adversarial Attack And Defence For Sentiment ClassificationCode0
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing—0
Self adversarial attack as an augmentation method for immunohistochemical stainings—0
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack—0
Boosting Adversarial Transferability through Enhanced Momentum—0
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition SystemsCode0
KoDF: A Large-scale Korean DeepFake Detection Dataset—0
Adversarial Attacks on Camera-LiDAR Models for 3D Car Detection—0
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
Towards Robust Speech-to-Text Adversarial Attack—0
Generating Unrestricted Adversarial Examples via Three Parameters—0
Internal Wasserstein Distance for Adversarial Attack and Defense—0
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling—0
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a BlinkCode1
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness VerificationCode1
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial AttackCode1
Practical Relative Order Attack in Deep RankingCode0
BASAR:Black-box Attack on Skeletal Action RecognitionCode1
Stabilized Medical Image AttacksCode0
Universal Adversarial Perturbations and Image Spam Classifiers—0
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial AttackCode0
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier DomainCode1
A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models—0
Online Adversarial AttacksCode1
A Brief Survey on Deep Learning Based Data Hiding—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