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 1051–1100 of 1808 papers

TitleStatusHype
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning—0
AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems—0
Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification—0
LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model—0
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise—0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network—0
Towards Adversarially Robust Deep Image Denoising—0
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks—0
Looking From the Future: Multi-order Iterations Can Enhance Adversarial Attack Transferability—0
Improving VAEs' Robustness to Adversarial Attack—0
L_p-norm Distortion-Efficient Adversarial Attack—0
L-RED: Efficient Post-Training Detection of Imperceptible Backdoor Attacks without Access to the Training Set—0
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack—0
MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models—0
Make the Most of Everything: Further Considerations on Disrupting Diffusion-based Customization—0
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification—0
AdvHaze: Adversarial Haze Attack—0
Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks—0
MARAGE: Transferable Multi-Model Adversarial Attack for Retrieval-Augmented Generation Data Extraction—0
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning—0
MathAttack: Attacking Large Language Models Towards Math Solving Ability—0
Maximal Jacobian-based Saliency Map Attack—0
AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems—0
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare—0
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Vulnerability of Appearance-based Gaze Estimation—0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack—0
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium—0
Metamorphic Adversarial Detection Pipeline for Face Recognition Systems—0
Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors—0
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy—0
Towards a Novel Perspective on Adversarial Examples Driven by Frequency—0
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs—0
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs—0
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy—0
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness—0
Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping—0
Minority Reports Defense: Defending Against Adversarial Patches—0
Adversarial training with perturbation generator networks—0
Mitigating Adversarial Attack for Compute-in-Memory Accelerator Utilizing On-chip Finetune—0
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers—0
Mixed Strategies for Security Games with General Defending Requirements—0
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems—0
Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks—0
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks—0
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios—0
Model Extraction Warning in MLaaS Paradigm—0
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition—0
Modeling Biological Immunity to Adversarial Examples—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