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Cited By

Leveraging Local Patch Differences in Multi-Object Scenes for Generative
Adversarial Attacks

v1v2 (latest)

Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks

IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2022

20 September 2022

S. Krishnamurthy

Amit K. Roy-Chowdhury

ArXiv (abs)PDF HTML Github

Papers citing "Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks"

5 / 5 papers shown

PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches

PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial PatchesComputer Vision and Pattern Recognition (CVPR), 2025

237

0

0

30 May 2025

Out-of-Bounding-Box Triggers: A Stealthy Approach to Cheat Object
Detectors

Out-of-Bounding-Box Triggers: A Stealthy Approach to Cheat Object DetectorsEuropean Conference on Computer Vision (ECCV), 2024

Lijia Yu

285

2

0

14 Oct 2024

FACL-Attack: Frequency-Aware Contrastive Learning for Transferable
Adversarial Attacks

FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks

Kuk-Jin Yoon

413

12

0

30 Jul 2024

Prompt-Driven Contrastive Learning for Transferable Adversarial Attacks

Prompt-Driven Contrastive Learning for Transferable Adversarial AttacksEuropean Conference on Computer Vision (ECCV), 2024

Kuk-Jin Yoon

603

10

0

30 Jul 2024

Cross-Domain Video Anomaly Detection without Target Domain Adaptation

Cross-Domain Video Anomaly Detection without Target Domain AdaptationIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2022

Kuan-Chuan Peng

Amit K. Roy-Chowdhury

376

45

0

14 Dec 2022

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