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 1701–1725 of 1808 papers

TitleStatusHype
Application of Adversarial Examples to Physical ECG Signals—0
Physical-World Optical Adversarial Attacks on 3D Face Recognition—0
Sparse and Transferable Universal Singular Vectors Attack—0
A Perceptual Distortion Reduction Framework: Towards Generating Adversarial Examples with High Perceptual Quality and Attack Success Rate—0
OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training—0
White-Box Target Attack for EEG-Based BCI Regression Problems—0
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization—0
Classifier-independent Lower-Bounds for Adversarial Robustness—0
Distillation-Enhanced Physical Adversarial Attacks—0
Semantically Stealthy Adversarial Attacks against Segmentation Models—0
Distributed Estimation over Directed Graphs Resilient to Sensor Spoofing—0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks—0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge—0
DLOVE: A new Security Evaluation Tool for Deep Learning Based Watermarking Techniques—0
DMS: Addressing Information Loss with More Steps for Pragmatic Adversarial Attacks—0
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images—0
DODEM: DOuble DEfense Mechanism Against Adversarial Attacks Towards Secure Industrial Internet of Things Analytics—0
Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts?—0
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images—0
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks—0
Doppelganger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack—0
Double Backpropagation for Training Autoencoders against Adversarial Attack—0
DIP-Watermark: A Double Identity Protection Method Based on Robust Adversarial Watermark—0
Do we need entire training data for adversarial training?—0
DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation—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