Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 501–525 of 1746 papers

Title	Date	Tasks	Status
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training	Nov 5, 2024	Adversarial RobustnessDiversity	—Unverified
DiffPAD: Denoising Diffusion-based Adversarial Patch Decontamination	Oct 31, 2024	Adversarial RobustnessBinarization	CodeCode Available
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training	Oct 30, 2024	Adversarial RobustnessFairness	—Unverified
CausAdv: A Causal-based Framework for Detecting Adversarial Examples	Oct 29, 2024	Adversarial Robustnesscounterfactual	CodeCode Available
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions	Oct 26, 2024	Abstractive Text SummarizationAdversarial Robustness	—Unverified
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness	Oct 24, 2024	Adversarial RobustnessModel Selection	—Unverified
Conflict-Aware Adversarial Training	Oct 21, 2024	Adversarial Robustness	—Unverified
Beyond Pruning Criteria: The Dominant Role of Fine-Tuning and Adaptive Ratios in Neural Network Robustness	Oct 19, 2024	Adversarial RobustnessSensitivity	—Unverified
Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language Models	Oct 19, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available
A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models	Oct 18, 2024	Adversarial RobustnessAutonomous Driving	—Unverified
DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain	Oct 16, 2024	Adversarial Robustness	CodeCode Available
New Paradigm of Adversarial Training: Breaking Inherent Trade-Off between Accuracy and Robustness via Dummy Classes	Oct 16, 2024	Adversarial Robustness	CodeCode Available
Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks	Oct 14, 2024	Adversarial RobustnessBinary Classification	—Unverified
Out-of-Bounding-Box Triggers: A Stealthy Approach to Cheat Object Detectors	Oct 14, 2024	Adversarial RobustnessObject	CodeCode Available
Understanding Adversarially Robust Generalization via Weight-Curvature Index	Oct 10, 2024	Adversarial RobustnessGeneralization Bounds	—Unverified
Towards Assurance of LLM Adversarial Robustness using Ontology-Driven Argumentation	Oct 10, 2024	Adversarial RobustnessCode Translation	—Unverified
Adversarial Robustness Overestimation and Instability in TRADES	Oct 10, 2024	Adversarial Robustness	—Unverified
Give me a hint: Can LLMs take a hint to solve math problems?	Oct 8, 2024	Adversarial RobustnessMath	CodeCode Available
Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models	Oct 8, 2024	Adversarial RobustnessTransfer Learning	—Unverified
Developing Assurance Cases for Adversarial Robustness and Regulatory Compliance in LLMs	Oct 4, 2024	Adversarial RobustnessManagement	—Unverified
Knowledge-Augmented Reasoning for EUAIA Compliance and Adversarial Robustness of LLMs	Oct 4, 2024	Adversarial Robustness	—Unverified
A Brain-Inspired Regularizer for Adversarial Robustness	Oct 4, 2024	Adversarial Robustness	CodeCode Available
Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs	Oct 4, 2024	Adversarial Robustness	—Unverified
LLM Safeguard is a Double-Edged Sword: Exploiting False Positives for Denial-of-Service Attacks	Oct 3, 2024	Adversarial RobustnessSafety Alignment	—Unverified
MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning	Oct 2, 2024	Adversarial RobustnessRepresentation Learning	—Unverified

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All datasets AdvGLUE CIFAR-10 ImageNet ImageNet-A ImageNet-C Stylized ImageNet CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeBERTa (single model)	Accuracy	0.61	—	Unverified
2	ALBERT (single model)	Accuracy	0.59	—	Unverified
3	T5 (single model)	Accuracy	0.57	—	Unverified
4	SMART_RoBERTa (single model)	Accuracy	0.54	—	Unverified
5	FreeLB (single model)	Accuracy	0.5	—	Unverified
6	RoBERTa (single model)	Accuracy	0.5	—	Unverified
7	InfoBERT (single model)	Accuracy	0.46	—	Unverified
8	ELECTRA (single model)	Accuracy	0.42	—	Unverified
9	BERT (single model)	Accuracy	0.34	—	Unverified
10	SMART_BERT (single model)	Accuracy	0.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed classifier	Accuracy	95.23	—	Unverified
2	Stochastic-LWTA/PGD/WideResNet-34-10	Accuracy	92.26	—	Unverified
3	Stochastic-LWTA/PGD/WideResNet-34-5	Accuracy	91.88	—	Unverified
4	GLOT-DR	Accuracy	84.13	—	Unverified
5	TRADES-ANCRA/ResNet18	Accuracy	81.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (SGD, Cosine)	Accuracy	77.4	—	Unverified
2	ResNet-50 (SGD, Step)	Accuracy	76.9	—	Unverified
3	DeiT-S (AdamW, Cosine)	Accuracy	76.8	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	76.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	12.2	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	3.3	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	3.2	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	3.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (AdamW, Cosine)	mean Corruption Error (mCE)	59.3	—	Unverified
2	ResNet-50 (SGD, Step)	mean Corruption Error (mCE)	57.9	—	Unverified
3	ResNet-50 (SGD, Cosine)	mean Corruption Error (mCE)	56.9	—	Unverified
4	DeiT-S (AdamW, Cosine)	mean Corruption Error (mCE)	48	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	13	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	8.4	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	8.3	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	8.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed Classifier	Clean Accuracy	85.21	—	Unverified
2	ResNet18/MART-ANCRA	Clean Accuracy	60.1	—	Unverified