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 1701–1750 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
AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack—0
D-square-B: Deep Distribution Bound for Natural-looking Adversarial Attack—0
DTA: Physical Camouflage Attacks using Differentiable Transformation Network—0
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing—0
SSCAE: A Novel Semantic, Syntactic, and Context-Aware Natural Language Adversarial Example Generator—0
SSCAE -- Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator—0
SSMI: How to Make Objects of Interest Disappear without Accessing Object Detectors?—0
Dynamic backdoor attacks against federated learning—0
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness—0
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning—0
STA: Adversarial Attacks on Siamese Trackers—0
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition—0
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks—0
Stabilized Medical Attacks—0
A Bayes-Optimal View on Adversarial Examples—0
A Non-monotonic Smooth Activation Function—0
Effective black box adversarial attack with handcrafted kernels—0
Effective faking of verbal deception detection with target-aligned adversarial attacks—0
Effects of Forward Error Correction on Communications Aware Evasion Attacks—0
Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models—0
Stabilizing Deep Tomographic Reconstruction—0
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense—0
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks—0
Adversarial Attack for Explanation Robustness of Rationalization Models—0
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training—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