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研究員  |  呂俊賢  
 
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Research Descriptions
 

1. Major achievements in the past 5 years

My recent research interests focus on the (Deep) Compressive Sensing and AI Security & Privacy issues. Compressive Sensing has attracted much attention due to its new paradigm of simultaneous sampling and compression. Our representative results are describe below.

Topic 1: Distributed Compressive Sensing (DCS) 

     Distributed compressive sensing (DCS) is a framework that considers joint sparsity within signal ensembles along with multiple measurement vectors (MMVs). However, current theoretical bounds of the probability of perfect recovery for MMVs are derived to be essentially identical to that of a single MV (SMV); this is because characteristics of the signal ensemble are ignored. In this work, our contribution is to complete the proof in that, by taking the size of signal ensembles into consideration, MMVs indeed exhibit better performance than SMV. 

Sung-Hsien Hsieh, Wei-Jie, Liang, Chun-Shien Lu, and Soo-Chang Pei, ``Distributed Compressive Sensing: Performance Analysis with Diverse Signal Ensembles,’’ IEEE Trans. on Signal Processing, vol. 68, pp. 3500-3514, 2020.

Topic 2: Compressed Sensing of Large-Scale Images

Cost-efficient compressive sensing of large-scale images with quickly reconstructed high-quality results is very challenging. In this work, we present an algorithm to solve convex optimization via the tree structure sparsity pattern, which can be run in the operator to reduce computation cost and maintain good quality, especially for large-scale images.

Wei-Jie, Liang, Gang-Xuan Lin, and Chun-Shien Lu, ``Tree Structure Sparsity Pattern Guided Convex Optimization for Compressive Sensing of Large-Scale Images,’’ IEEE Trans. on Image Processing, Vol. 26, No. 2, pp. 847-859, 2017.

Topic 3: Compressed Sensing-Based Clone Identification in Sensor Network

Clone detection, aimed at detecting illegal copies with all of the credentials of legitimate sensor nodes, is of great importance for sensor networks because of the severe impact of clones on network operations. Various detection methods have been proposed, but most of them are communication-inefficient. In view of the sparse characteristic of replicated nodes, we propose a novel clone detection framework, called CSI, based on compressed sensing.

Chia-Mu Yu, Chun-Shien Lu, and Sy-Yen Kuo, “Compressed Sensing-Based Clone Identification in Sensor Network,’’ IEEE Trans. on Wireless Communications, Vol. 15, No. 4, pp. 3071-3084, 2016.

 

The amount of publications, pertaining to AI Security and Privacy, have been grown exponentially since 2014. This indicates that the issues of AI security and privacy has received much attention recently. Our representative results are describe below.

Topic 1: Difference-Seeking Generative Adversarial Network--Unseen Sample Generation

Unseen data, which are not samples from the distribution of training data and are difficult to collect, have exhibited importance in numerous applications, (e.g., novelty detection, semi-supervised learning, and adversarial training). In this paper, we introduce a general framework called difference-seeking generative adversarial network (DSGAN), to generate various types of unseen data.

Yi-Lin Sung, Sung-Hsien Hsieh, Soo-Chang Pei, and Chun-Shien Lu, ``Difference-Seeking Generative Adversarial Network--Unseen Sample Generation,’’ International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 26-30, 2020.

Topic 2: Perceptual Differential Privacy in Images

With the growing use of camera devices, the industry has many image datasets that provide more opportunities for collaboration between the machine learning community and industry. However, the sensitive information discourage data owners from releasing these datasets. In our work, we propose perceptual indistinguishability (PI) as a formal privacy notion particularly for images. We then propose PI-Net, a privacy-preserving mechanism that achieves image obfuscation with PI guarantee.

Jia-Wei Chen, Li-Ju Chen, Chia-Mu Yu, and Chun-Shien Lu, ``Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semantics,’’ CVPR, June 19-25, 2021.

2.    Future Plans

Following our experiences in compressive sensing, deep learning, and AI/Multimedia security & privacy, we have two future works.

Deep Sensing: Deep sensing is a new learning model in solving optimization problems for multi-tasks. To this end, we integrate deep learning and compressive sensing to solve the image inverse problems, including compressive sensing, super-resolution, inpainting, and so on. To our knowledge, we have not found any work in the literature that studied such optimization-based neural network to solve the generalized multi-task inverse problems. The challenge is, for each task, how to approximate its state-of-the-art performance.

AI Security with Robust Training: Considering the well-trained NN models should possess larger certified radii, we investigate ``Smoothed Robust Training’’ in three possible ways. First, we will design a NN model to have small Lipschitz constant, despite that it is challenging to evaluate the Lipschitz constant for each layer. Second, the training strategy is concerned in that we aim to achieve robust training by virtue of data augmentation. Finally, in view of the importance of loss function design, we plan to incorporate the optimization problem in solving the certified radius with loss function to maximize certified radius.

 
 
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