LYU Mingzhi

Appointment: PhD student

Research topics:

• Adversarial Examples for Proactively Protecting Images against DeepFake and DeepNude

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1 to 10 of 50 Results

Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Jan 23, 2024 Lyu, Mingzhi; Huang, Yi; Kong, Adams Wai-Kin, 2024, "Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers", https://doi.org/10.21979/N9/JNH3P4, DR-NTU (Data), V1 This dataset contains the main codes for the paper "Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers", which proposes a method to protect visible watermark on images against deep-learning-based watermark removers.
blind_watermark_removal.jpg Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers JPEG Image - 78.9 KB - MD5: 38421b45b03d549ffe23d59dd27e1811 Documentation
Crfill.cpython-36.pyc Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Unknown - 3.9 KB - MD5: b8a8e1900a27bf07276ddcd2e405f314
Crfill.cpython-38.pyc Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Unknown - 3.9 KB - MD5: a0a9e1e391631d61b2a73e97a62b9fa9
Crfill.py Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Python Source Code - 5.1 KB - MD5: a8a0281bfde0d2a1e870d117c9334ed6
DBWEModel.cpython-36.pyc Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Unknown - 2.7 KB - MD5: 7582b957b2858c5847b027137b984f45
DBWEModel.cpython-38.pyc Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Unknown - 2.7 KB - MD5: 20482ffa1467c41c163cd77c857f7255
DBWEModel.py Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Python Source Code - 3.9 KB - MD5: e1d0faf9ead73a1f62cc493144da1cc4
EdgeCon.cpython-36.pyc Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Unknown - 4.1 KB - MD5: 398280f28c2c71e85ce6d1b4c3c34336
EdgeCon.cpython-38.pyc Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Unknown - 4.0 KB - MD5: b97302cab956cb841a280dc162543bb0

Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers

Jan 23, 2024

Lyu, Mingzhi; Huang, Yi; Kong, Adams Wai-Kin, 2024, "Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers", https://doi.org/10.21979/N9/JNH3P4, DR-NTU (Data), V1

This dataset contains the main codes for the paper "Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers", which proposes a method to protect visible watermark on images against deep-learning-based watermark removers.

blind_watermark_removal.jpg

Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers

JPEG Image - 78.9 KB -

Documentation

Crfill.cpython-36.pyc

Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers

Unknown - 3.9 KB -

Crfill.cpython-38.pyc

Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers

Unknown - 3.9 KB -

Crfill.py

Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers