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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 211–220 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Attack On Yolov5 For Traffic And Road Sign Detection	May 27, 2023	Adversarial Attackobject-detection	CodeCode Available	1	5
Boosting the Transferability of Video Adversarial Examples via Temporal Translation	Oct 18, 2021	Adversarial AttackTranslation	CodeCode Available	1	5
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs	Oct 6, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models	Feb 16, 2025	Adversarial AttackBackdoor Attack	CodeCode Available	1	5
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models	Jul 24, 2023	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows	Jul 15, 2020	Adversarial Attack	CodeCode Available	1	5
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework	Oct 28, 2021	Adversarial AttackLanguage Modelling	CodeCode Available	1	5
On Evaluating Adversarial Robustness	Feb 18, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
CausalAdv: Adversarial Robustness through the Lens of Causality	Jun 11, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data	Mar 12, 2025	Adversarial AttackMalware Analysis	CodeCode Available	1	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