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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 81–90 of 1808 papers

Title	Date	Tasks	Status	Hype
Bitstream Collisions in Neural Image Compression via Adversarial Perturbations	Mar 25, 2025	Adversarial AttackImage Compression	CodeCode Available	0
Make the Most of Everything: Further Considerations on Disrupting Diffusion-based Customization	Mar 18, 2025	Adversarial Attack	—Unverified	0
Augmented Adversarial Trigger Learning	Mar 16, 2025	Adversarial Attack	—Unverified	0
CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data	Mar 12, 2025	Adversarial AttackMalware Analysis	CodeCode Available	1
ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks	Mar 10, 2025	Adversarial AttackDeep Learning	—Unverified	0
Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution	Mar 6, 2025	Adversarial AttackSuper-Resolution	—Unverified	0
Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients	Mar 5, 2025	Adversarial Attack	CodeCode Available	0
Decoder Gradient Shield: Provable and High-Fidelity Prevention of Gradient-Based Box-Free Watermark Removal	Feb 28, 2025	Adversarial AttackDecoder	—Unverified	0
Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior	Feb 28, 2025	Adversarial Attack	CodeCode Available	1
QFAL: Quantum Federated Adversarial Learning	Feb 28, 2025	Adversarial AttackFederated Learning	—Unverified	0

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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