Update README.md

README.md

@@ -10,17 +10,19 @@ tags:
 - code
 ---
 
-# Low Quality Live Attacks
-The dataset includes live-recorded Anti-Spoofing videos from around the world, captured via **low-quality** webcams with resolutions like QVGA, QQVGA and QCIF.
+# Low Quality Live Attacks - Biometric Attack dataset
+The anti spoofing dataset includes live-recorded Anti-Spoofing videos from around the world, captured via **low-quality** webcams with resolutions like QVGA, QQVGA and QCIF. The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users. 
 
-# Get the dataset
+The dataset contains images and videos of real humans with various **views, and colors**, making it a comprehensive resource for researchers working on anti-spoofing technologies.
 
-### This is just an example of the data
-
-Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market/low-quality-webcam-attacks?utm_source=huggingface&utm_medium=cpc&utm_campaign=low_quality_webcam_video_attacks) to discuss your requirements, learn about the price and buy the dataset.
+# 💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on **[TrainingData](https://trainingdata.pro/datasets/low-quality-webcam-attacks?utm_source=huggingface&utm_medium=cpc&utm_campaign=low_quality_webcam_video_attacks)** to buy the dataset
 
 ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F43bc66b1f16995fb42f10075db8f9ba5%2F4.png?generation=1684704084546644&alt=media)
 
+The dataset provides data to combine and apply different techniques, approaches, and models to address the challenging task of distinguishing between genuine and spoofed inputs, providing effective anti-spoofing solutions in active authentication systems. These solutions are crucial as newer devices, such as phones, have become vulnerable to spoofing attacks due to the availability of technologies that can create replays, reflections, and depths, making them susceptible to spoofing and generalization. 
+
+Our dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models. 
+
 # Webcam Resolution
 The collection of different video resolutions is provided, like:
 - QVGA (320p x 240p), 
@@ -42,8 +44,12 @@ Additionally, the model of the webcam is also specified.
 
 Metadata is represented in the `file_info.csv`.
 
-## [**TrainingData**](https://trainingdata.pro/data-market/low-quality-webcam-attacks?utm_source=huggingface&utm_medium=cpc&utm_campaign=low_quality_webcam_video_attacks) provides high-quality data annotation tailored to your needs
+# 💴 Buy the Dataset: This is just an example of the data. Leave a request on **[https://trainingdata.pro](https://trainingdata.pro/datasets/low-quality-webcam-attacks?utm_source=huggingface&utm_medium=cpc&utm_campaign=low_quality_webcam_video_attacks)** to discuss your requirements, learn about the price and buy the dataset.
+
+## **[TrainingData](https://trainingdata.pro/datasets/low-quality-webcam-attacks?utm_source=huggingface&utm_medium=cpc&utm_campaign=low_quality_webcam_video_attacks)** provides high-quality data annotation tailored to your needs
 
 More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets
 
-TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
+TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
+
+*keywords: liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, ibeta dataset, human video dataset, video dataset, low quality video dataset, phone attack dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset*