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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 261–270 of 1808 papers

Title	Date	Tasks	Status	Hype
Attacking Video Recognition Models with Bullet-Screen Comments	Oct 29, 2021	Adversarial AttackAdversarial Attack on Video Classification	CodeCode Available	1
Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box Framework	May 24, 2025	Adversarial AttackSpeech Tokenization	CodeCode Available	1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models	Jul 24, 2023	Adversarial AttackAdversarial Defense	CodeCode Available	1
Are AlphaZero-like Agents Robust to Adversarial Perturbations?	Nov 7, 2022	Adversarial AttackBoard Games	CodeCode Available	1
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection	Dec 14, 2023	Adversarial AttackAttribute	CodeCode Available	1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction	Jan 16, 2022	Adversarial AttackCombinatorial Optimization	CodeCode Available	1
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label	Jul 1, 2022	Adversarial AttackBackdoor Attack	CodeCode Available	1
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models	Oct 23, 2023	Adversarial AttackBlocking	CodeCode Available	1
BayesOpt Adversarial Attack	May 1, 2020	Adversarial AttackBayesian Optimisation	CodeCode Available	1
Black-box Adversarial Example Generation with Normalizing Flows	Jul 6, 2020	Adversarial Attack	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