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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
RAIN: Your Language Models Can Align Themselves without Finetuning	Sep 13, 2023	Adversarial AttackTruthfulQA	CodeCode Available	1
Differentiable JPEG: The Devil is in the Details	Sep 13, 2023	Adversarial AttackQuantization	CodeCode Available	1
Certifying LLM Safety against Adversarial Prompting	Sep 6, 2023	Adversarial AttackLanguage Modelling	CodeCode Available	1
PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification	Aug 22, 2023	Adversarial AttackBackdoor Attack	CodeCode Available	1
On the Adversarial Robustness of Multi-Modal Foundation Models	Aug 21, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient	Aug 10, 2023	Action RecognitionActivity Recognition	CodeCode Available	1
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability	Aug 5, 2023	Adversarial Attack	CodeCode Available	1
Multi-attacks: Many images + the same adversarial attack many target labels	Aug 4, 2023	Adversarial Attack	CodeCode Available	1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models	Jul 24, 2023	Adversarial AttackAdversarial Defense	CodeCode Available	1
OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples	Jul 21, 2023	Adversarial AttackAdversarial Attack Detection	CodeCode Available	1

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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