Thesis

Title

Introduction a method for Zero-Shot Face Anti-Spoofing

Table of Contents

• Report
• Papers
• Papers Summary
• Datasets
• Other Resources

Report

• see this [file].

Papers

2021

pattern recognition letters

• W. Guo, B. Tondi, and M. Barni, “A Master Key backdoor for universal impersonation attack against DNN-based face verification”. [PDF]

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2020

CVPR

• H. Dang, F. Liu, J. Stehouwer, X. Liu, and A. Jain, “On the Detection of Digital Face Manipulation”. [PDF] [code]

Computer Vision and Image Understanding

• B. Zhang, B. Tondi, and M. Barni, “Adversarial examples for replay attacks against CNN-based face recognition with anti-spoofing capability” [PDF]

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2019

CVPR

• Y. Liu, J. Stehouwer, A. Jourabloo, and X. Liu, “Deep tree learning for zero-shot face anti-spoofing”. [PDF] [code]

IJCNN

• C. Nagpal and S. R. Dubey, “A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing”. [PDF]

ACM Transactions on Intelligent Systems and Technology

• W. Wang, V. W. Zheng, H. Yu, and C. Miao, “A Survey of Zero-Shot Learning”. [PDF]

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2015

ICML

• G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese Neural Networks for One-shot Image Recognition”. [PDF] [code]
• K. Xu et al., “Show, attend and tell: Neural image caption generation with visual attention”. [PDF] [code]

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2014

IEEE Transactions on Pattern Analysis and Machine Intelligence

• C. H. Lampert, H. Nickisch, and S. Harmeling, “Attribute-Based Classification for Zero-Shot Visual Object Categorization”. [PDF]

Papers-Summary

• I summarised some papers in Persian.
• watch [here], I'll update it.

Datasets

• LAD: Large-scale Attribute Dataset. Categories:230. [link]
• CUB: Caltech-UCSD Birds. Categories:200. [link]
• AWA2: Animals with Attributes. Categories:50. [link]
• aPY: attributes Pascal and Yahoo. Categories:32 [link]
• Flowers Dataset: There are two datasets, Categories: 17 and 102. [link]
• SUN: Scene Attributes. Categories:717. [link]
• HMDB51 : a large human motion database. Categories:51 [link]
• UCF101 : an action image recognition dataset of real action videos, collected from YouTube. Categories:101 [link]
• DFFD : The DFFD dataset combines multiple forgery types in a single dataset [link]
• SiW : face anti-spoofing database named Spoof in the Wild (SiW) database [link]
• CASIA-SURF : Multi-modal Face Anti-spoofing (Presentation Attack Detection [link]
• Data Splits and Features for CUB, AWA1, AWA2, SUN and APY: [link]
• REPLAY-MOBILE : a dataset for face recognition and presentation attack detection (anti-spoofing) [link]