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

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Showing 1151–1200 of 1808 papers

TitleStatusHype
UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples—0
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks—0
Uncertainty Measurement of Deep Learning System based on the Convex Hull of Training Sets—0
Undersensitivity in Neural Reading Comprehension—0
Understanding Model Ensemble in Transferable Adversarial Attack—0
Understanding Oversmoothing in GNNs as Consensus in Opinion Dynamics—0
Understanding Pose and Appearance Disentanglement in 3D Human Pose Estimation—0
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification—0
Bidirectional Contrastive Split Learning for Visual Question Answering—0
Universal Adversarial Attack on Aligned Multimodal LLMs—0
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet—0
Universal Adversarial Attack on Deep Learning Based Prognostics—0
Universal Adversarial Attack Using Very Few Test Examples—0
Universal Adversarial Perturbations and Image Spam Classifiers—0
Universal Attacks on Equivariant Networks—0
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning—0
Classifier-independent Lower-Bounds for Adversarial Robustness—0
Universal Soldier: Using Universal Adversarial Perturbations for Detecting Backdoor Attacks—0
Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models—0
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection—0
Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification—0
AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation—0
Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models—0
Untargeted Adversarial Attack on Knowledge Graph Embeddings—0
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series—0
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System—0
Using an ensemble color space model to tackle adversarial examples—0
Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples—0
Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks—0
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness—0
Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries—0
Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis—0
Variation Enhanced Attacks Against RRAM-based Neuromorphic Computing System—0
VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning—0
Visual Adversarial Attack on Vision-Language Models for Autonomous Driving—0
Visual Attack and Defense on Text—0
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise—0
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks—0
Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks—0
Vulnerability of Appearance-based Gaze Estimation—0
Vulnerability of Deep Learning—0
Wasserstein Adversarial Examples on Univariant Time Series Data—0
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks—0
Watertox: The Art of Simplicity in Universal Attacks A Cross-Model Framework for Robust Adversarial Generation—0
Wavelet-Based Image Tokenizer for Vision Transformers—0
Wavelets Beat Monkeys at Adversarial Robustness—0
Weighted-Sampling Audio Adversarial Example Attack—0
Weight Map Layer for Noise and Adversarial Attack Robustness—0
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images—0
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective—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