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 576–600 of 1808 papers

TitleStatusHype
Adversarial Exposure Attack on Diabetic Retinopathy Imagery Grading—0
Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models—0
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models—0
Adversarial Attack by Limited Point Cloud Surface Modifications—0
Architecture Selection via the Trade-off Between Accuracy and Robustness—0
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement—0
Adversarial Attack Based on Prediction-Correction—0
Adversarial Examples in Deep Learning: Characterization and Divergence—0
Improving VAEs' Robustness to Adversarial Attack—0
A Practical and Stealthy Adversarial Attack for Cyber-Physical Applications—0
A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems—0
Effective faking of verbal deception detection with target-aligned adversarial attacks—0
Effects of Forward Error Correction on Communications Aware Evasion Attacks—0
Applying Tensor Decomposition to image for Robustness against Adversarial Attack—0
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks—0
Application of Adversarial Examples to Physical ECG Signals—0
Adversarial Examples for Model-Based Control: A Sensitivity Analysis—0
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space—0
Physical-World Optical Adversarial Attacks on 3D Face Recognition—0
Adversarial Example Detection Using Latent Neighborhood Graph—0
A Perceptual Distortion Reduction Framework: Towards Generating Adversarial Examples with High Perceptual Quality and Attack Success Rate—0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique—0
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization—0
Adversarial Attack and Defense on Point Sets—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