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 1276–1300 of 1808 papers

TitleStatusHype
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
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
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
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
Practical Relative Order Attack in Deep RankingCode0
Stabilized Medical Image AttacksCode0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet20Test Accuracy89.9589.95(1)Community Verified
2Xu et al.Attack: PGD2078.68—Unverified
33-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
4TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
5AdvTraining [madry2018]Attack: PGD2048.44—Unverified
6TRADES [zhang2019b]Attack: PGD2045.9—Unverified
7XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified