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 1001–1050 of 1808 papers

TitleStatusHype
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability—0
Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation—0
Salient Information Preserving Adversarial Training Improves Clean and Robust Accuracy—0
Sample Complexity of an Adversarial Attack on UCB-based Best-arm Identification Policy—0
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks—0
SAR-AE-SFP: SAR Imagery Adversarial Example in Real Physics domain with Target Scattering Feature Parameters—0
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers—0
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator—0
Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution—0
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses—0
Scaling Laws for Black box Adversarial Attacks—0
A^3D: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks—0
Search Space of Adversarial Perturbations against Image Filters—0
Second-Order Adversarial Attack and Certifiable Robustness—0
Second-Order NLP Adversarial Examples—0
Second Order State Hallucinations for Adversarial Attack Mitigation in Formation Control of Multi-Agent Systems—0
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability—0
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples—0
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks—0
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack—0
Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models—0
Seeing the Threat: Vulnerabilities in Vision-Language Models to Adversarial Attack—0
Seeking Flat Minima over Diverse Surrogates for Improved Adversarial Transferability: A Theoretical Framework and Algorithmic Instantiation—0
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation—0
Self adversarial attack as an augmentation method for immunohistochemical stainings—0
SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner—0
SELF-KNOWLEDGE DISTILLATION ADVERSARIAL ATTACK—0
Self-Supervised Adversarial Example Detection by Disentangled Representation—0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Robust Pretrained Language Models—0
Self-Supervised Representation Learning for Adversarial Attack Detection—0
Semantic Adversarial Attacks on Face Recognition through Significant Attributes—0
Semantic Autoencoder and Its Potential Usage for Adversarial Attack—0
Semantic Image Attack for Visual Model Diagnosis—0
Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm—0
Model Robustness with Text Classification: Semantic-preserving adversarial attacks—0
SemDiff: Generating Natural Unrestricted Adversarial Examples via Semantic Attributes Optimization in Diffusion Models—0
SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images—0
Sequential Attacks on Agents for Long-Term Adversarial Goals—0
SHARP: Search-Based Adversarial Attack for Structured Prediction—0
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness—0
Should Adversarial Attacks Use Pixel p-Norm?—0
SIGL: Securing Software Installations Through Deep Graph Learning—0
Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack—0
Similarity of Neural Architectures using Adversarial Attack Transferability—0
Simple and Effective Stochastic Neural Networks—0
Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis—0
SMART: Skeletal Motion Action Recognition aTtack—0
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization—0
Snowball Adversarial Attack on Traffic Sign Classification—0
Solving Non-Convex Non-Differentiable Min-Max Games using Proximal Gradient Method—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