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 1776–1800 of 1808 papers

TitleStatusHype
Enhancing TinyML Security: Study of Adversarial Attack Transferability—0
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks—0
Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing—0
Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier—0
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling—0
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms—0
An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter—0
ErasableMask: A Robust and Erasable Privacy Protection Scheme against Black-box Face Recognition Models—0
Adversarial Attack by Limited Point Cloud Surface Modifications—0
Evading Detection Actively: Toward Anti-Forensics against Forgery Localization—0
EVALOOP: Assessing LLM Robustness in Programming from a Self-consistency Perspective—0
Stochastic Variance Reduced Ensemble Adversarial Attack—0
Evaluating Adversarial Robustness on Document Image Classification—0
Adversarial Attack Based on Prediction-Correction—0
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication—0
Evaluating Neural Model Robustness for Machine Comprehension—0
Attacking c-MARL More Effectively: A Data Driven Approach—0
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack—0
Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents—0
Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models—0
Evaluating the Robustness of LiDAR Point Cloud Tracking Against Adversarial Attack—0
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models—0
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs—0
Evaluation of Four Black-box Adversarial Attacks and Some Query-efficient Improvement Analysis—0
Evaluation of Momentum Diverse Input Iterative Fast Gradient Sign Method (M-DI2-FGSM) Based Attack Method on MCS 2018 Adversarial Attacks on Black Box Face Recognition System—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