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 1751–1775 of 1808 papers

TitleStatusHype
Standard detectors aren't (currently) fooled by physical adversarial stop signs—0
State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning—0
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks—0
Efficient universal shuffle attack for visual object tracking—0
EFSG: Evolutionary Fooling Sentences Generator—0
Embodied Laser Attack:Leveraging Scene Priors to Achieve Agent-based Robust Non-contact Attacks—0
Emoti-Attack: Zero-Perturbation Adversarial Attacks on NLP Systems via Emoji Sequences—0
Emotion Loss Attacking: Adversarial Attack Perception for Skeleton based on Multi-dimensional Features—0
Empirical Study of the Decision Region and Robustness in Deep Neural Networks—0
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model—0
Energy Attack: On Transferring Adversarial Examples—0
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations—0
State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems—0
Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security—0
Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning—0
Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack—0
3DGAA: Realistic and Robust 3D Gaussian-based Adversarial Attack for Autonomous Driving—0
Enhancing Adversarial Transferability via Component-Wise Transformation—0
Enhancing Adversarial Transferability with Checkpoints of a Single Model's Training—0
Universal Soldier: Using Universal Adversarial Perturbations for Detecting Backdoor Attacks—0
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning—0
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation—0
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples—0
Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation—0
Enhancing the Transferability via Feature-Momentum 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