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 676–700 of 1808 papers

TitleStatusHype
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks—0
Adversarial Examples in Deep Learning: Characterization and Divergence—0
Forbidden Facts: An Investigation of Competing Objectives in Llama-2—0
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search—0
Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models—0
Effective faking of verbal deception detection with target-aligned adversarial attacks—0
Effects of Forward Error Correction on Communications Aware Evasion Attacks—0
Defensive Quantization: When Efficiency Meets Robustness—0
Adversarial Attack with Raindrops—0
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models—0
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models—0
FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models—0
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack—0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection—0
Frequency-Tuned Universal Adversarial Attacks—0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—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
Defense-guided Transferable Adversarial Attacks—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
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks—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