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 1751–1800 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
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