Search Results for author: Tiantian Li

Found 5 papers, 3 papers with code

BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis

1 code implementation10 Apr 2022 Yuexin Xiang, Yuchen Lei, Ding Bao, Wei Ren, Tiantian Li, Qingqing Yang, Wenmao Liu, Tianqing Zhu, Kim-Kwang Raymond Choo

Cryptocurrencies are no longer just the preferred option for cybercriminal activities on darknets, due to the increasing adoption in mainstream applications.

A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep Learning

1 code implementation19 May 2021 Yuexin Xiang, Tiantian Li, Wei Ren, Tianqing Zhu, Kim-Kwang Raymond Choo

Experimental findings on the testing set show that our scheme preserves image privacy while maintaining the availability of the training set in the deep learning models.

Data Augmentation Image Classification +1

Generating Image Adversarial Examples by Embedding Digital Watermarks

2 code implementations14 Aug 2020 Yuexin Xiang, Tiantian Li, Wei Ren, Tianqing Zhu, Kim-Kwang Raymond Choo

We devise an efficient mechanism to select host images and watermark images and utilize the improved discrete wavelet transform (DWT) based Patchwork watermarking algorithm with a set of valid hyperparameters to embed digital watermarks from the watermark image dataset into original images for generating image adversarial examples.

Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images

no code implementations ECCV 2020 Yuting He, Tiantian Li, Guanyu Yang, Youyong Kong, Yang Chen, Huazhong Shu, Jean-Louis Coatrieux, Jean-Louis Dillenseger, Shuo Li

Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation.

Image Registration Medical Image Registration

Semi-supervised learning method based on predefined evenly-distributed class centroids

no code implementations13 Jan 2020 Qiuyu Zhu, Tiantian Li

Meanwhile, for unlabeled samples, we also use KL divergence to constrain the consistency of the network predictions between unlabeled and augmented samples.

Data Augmentation General Classification +1

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