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 551–600 of 1808 papers

TitleStatusHype
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks—0
Single-Class Target-Specific Attack against Interpretable Deep Learning SystemsCode0
Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-IdentificationCode0
Brightness-Restricted Adversarial Attack Patch—0
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey—0
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection—0
Post-train Black-box Defense via Bayesian Boundary Correction—0
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack—0
Towards Sybil Resilience in Decentralized Learning—0
Cross-lingual Cross-temporal Summarization: Dataset, Models, EvaluationCode0
Adversarial Attacks Neutralization via Data Set Randomization—0
Sample Attackability in Natural Language Adversarial AttacksCode0
Physics-constrained Attack against Convolution-based Human Motion PredictionCode0
You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks—0
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models—0
Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systemsCode0
I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models—0
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions—0
COVER: A Heuristic Greedy Adversarial Attack on Prompt-based Learning in Language Models—0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers—0
Expanding Scope: Adapting English Adversarial Attacks to ChineseCode0
Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering ApproachCode0
A Robust Likelihood Model for Novelty Detection—0
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception—0
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language ExplanationsCode0
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy—0
Adversarial Attack Based on Prediction-Correction—0
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search—0
Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach—0
Graph-based methods coupled with specific distributional distances for adversarial attack detectionCode0
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation FrameworkCode0
Adversarial Attack On Yolov5 For Traffic And Road Sign DetectionCode1
Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision MakingCode0
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation—0
Another Dead End for Morphological Tags? Perturbed Inputs and ParsingCode0
Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning—0
Attribute-Guided Encryption with Facial Texture Masking—0
Latent Magic: An Investigation into Adversarial Examples Crafted in the Semantic Latent Space—0
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial AttacksCode1
Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial AttackCode0
Dynamic Transformers Provide a False Sense of EfficiencyCode0
Spatial-Frequency Discriminability for Revealing Adversarial PerturbationsCode0
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend—0
Content-based Unrestricted Adversarial Attack—0
Iterative Adversarial Attack on Image-guided Story Ending Generation—0
Attacking Perceptual Similarity Metrics—0
Diffusion Models for Imperceptible and Transferable Adversarial AttackCode2
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search—0
The Best Defense is Attack: Repairing Semantics in Textual 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