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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 426–450 of 1808 papers

Title	Date	Tasks	Status	Score
Feature Space Perturbations Yield More Transferable Adversarial Examples	Jun 1, 2019	Adversarial Attack	CodeCode Available	5
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness	Nov 10, 2023	Adversarial AttackDiversity	CodeCode Available	5
BERTops: Studying BERT Representations under a Topological Lens	May 2, 2022	Adversarial Attack	CodeCode Available	5
FDA: Feature Disruptive Attack	Sep 10, 2019	Adversarial Attackimage-classification	CodeCode Available	5
Adversarial Attack Generation Empowered by Min-Max Optimization	Jun 9, 2019	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Adaptive Image Transformations for Transfer-based Adversarial Attack	Nov 27, 2021	Adversarial Attack	CodeCode Available	5
Fast Inference of Removal-Based Node Influence	Mar 13, 2024	Adversarial Attackcounterfactual	CodeCode Available	5
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting	Oct 20, 2023	Adversarial AttackGrammatical Error Correction	CodeCode Available	5
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations	Oct 17, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Adversarial Laser Spot: Robust and Covert Physical-World Attack to DNNs	Jun 2, 2022	Adversarial Attack	CodeCode Available	5
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality Metrics	Apr 11, 2023	Adversarial AttackNo-Reference Image Quality Assessment	CodeCode Available	5
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning	May 5, 2021	Adversarial AttackAnomaly Detection	CodeCode Available	5
Fashion-Guided Adversarial Attack on Person Segmentation	Apr 17, 2021	Adversarial AttackHuman Instance Segmentation	CodeCode Available	5
FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMs	Mar 20, 2024	Adversarial Attack	CodeCode Available	5
A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization	Dec 14, 2020	Adversarial Attack	CodeCode Available	5
BitAbuse: A Dataset of Visually Perturbed Texts for Defending Phishing Attacks	Feb 6, 2025	Adversarial Attack	CodeCode Available	5
Bitstream Collisions in Neural Image Compression via Adversarial Perturbations	Mar 25, 2025	Adversarial AttackImage Compression	CodeCode Available	5
Exploiting vulnerabilities of deep neural networks for privacy protection	Jul 19, 2020	Adversarial AttackQuantization	CodeCode Available	5
A Theoretical View of Linear Backpropagation and Its Convergence	Dec 21, 2021	Adversarial Attack	CodeCode Available	5
Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-Identification	Jul 8, 2023	Adversarial AttackPerson Re-Identification	CodeCode Available	5
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method	Jan 26, 2021	Adversarial AttackExplainable artificial intelligence	CodeCode Available	5
Explainable Graph Neural Networks Under Fire	Jun 10, 2024	Adversarial Attack	CodeCode Available	5
Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models	Oct 17, 2021	Adversarial AttackGraph Neural Network	CodeCode Available	5
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective	Jan 1, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	5

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Xu et al.	Attack: PGD20	78.68	—	Unverified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
3	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
4	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
5	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
6	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified