Efficient Privacy-Aware Imagery Data Analysis

The widespread use of smartphones and camera-coupled Internet of Thing (IoT) devices triggers an explosive growth of imagery data. To extract and process the rich contents contained in imagery data, various image analysis techniques have been investigated and applied to a spectrum of application scenarios. In recent years, breakthroughs in deep learning have powered a new revolution for image analysis in terms of effectiveness with high resource consumption. Given the fact that most smartphones and IoT devices have limited computational capability and battery life, they are not ready for the processing of computational intensive analytics over imagery data collected by them, especially when deep learning is involved. To resolve the bottleneck of computation, storage, and energy for these resource constrained devices, offloading complex image analysis to public cloud computing platforms has become a promising trend in both academia and industry. However, an outstanding challenge with public cloud is on the protection of sensitive information contained in many imagery data, such as personal identities and financial data. Directly sending imagery data to the public cloud can cause serious privacy concerns and even legal issues. In this dissertation, I propose a comprehensive privacy-preserving imagery data analysis framework which can be integrated in different application scenarios to assist image analysis for resource-constrained devices with efficiency, accuracy, and privacy protection. I first identify security challenges in the utilization of public cloud for image analysis. Then, I design and develop a set of novel solutions to address these challenges. These solutions will be featured by strong privacy guarantee, lightweight computation, low accuracy loss compared with image analysis without privacy protection. To optimize the communication overhead and resource utilization of using cloud computing, I investigate edge computing, which is a promising technique to ameliorate the high communication overhead in cloud-assisted architectures. Furthermore, to boost the performance of my solutions under both cloud and edge deployment, I also provide a set of pluggable enhancement modules to be applied to meet different requirements for various tasks. By exploring the features of edge computing and cloud computing, I flexibly incorporate them as a comprehensive framework to provide privacy-preserving image analysis services

A TiO2/polyurethane Composite for Photodegradation of Formaldehyde via Covalently Incorporation of Amino-functionalized TiO2

In this study, a TiO2/polyurethane composite (PU-TiO2) for photodegradation of formaldehyde was prepared based on TiO2 amino-functionalized with 3-aminopropyltriethoxysilane (APTS). FTIR, XPS, TG and TEM measurements demonstrated the successful amino-functionalization of TiO2 by APTS. Then the NH2-TiO2 were incorporated into polyurethane (PU) through urea linkages, and the thermal stability, mechanical properties as well as degradation efficiency of PU-TiO2 were investigated. PU-TiO2 possessed good thermal stability both in the storage and application. Compared with PU physically blended with TiO2, PU-TiO2 showed improved compatibility between PU and NH2-TiO2, evidenced by the enhanced mechanical properties. Most importantly, PU- TiO2 presented a good photoactivity for the degradation of formaldehyde

Parsing Objects at a Finer Granularity: A Survey

Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms, while the inherent relations between these tasks are neglected. Moreover, given most of the research remains fragmented, we conduct an in-depth study of the advanced work from a new perspective of learning the part relationship. In this perspective, we first consolidate recent research and benchmark syntheses with new taxonomies. Based on this consolidation, we revisit the universal challenges in fine-grained part segmentation and recognition tasks and propose new solutions by part relationship learning for these important challenges. Furthermore, we conclude several promising lines of research in fine-grained visual parsing for future research.Comment: Survey for fine-grained part segmentation and object recognition; Accepted by Machine Intelligence Research (MIR

CAPIA: Cloud Assisted Privacy-Preserving Image Annotation

Using public cloud for image storage has become a prevalent trend with the rapidly increasing number of pictures generated by various devices. For example, today\u27s most smartphones and tablets synchronize photo albums with cloud storage platforms. However, as many images contain sensitive information, such as personal identities and financial data, it is concerning to upload images to cloud storage. To eliminate such privacy concerns in cloud storage while keeping decent data management and search features, a spectrum of keywords-based searchable encryption (SE) schemes have been proposed in the past decade. Unfortunately, there is a fundamental gap remains open for their support of images, i.e., appropriate keywords need to be extracted for images before applying SE schemes to them. On one hand, it is obviously impractical for smartphone users to manually annotate their images. On the other hand, although cloud storage services now offer image annotation services, they rely on access to users\u27 unencrypted images. To fulfill this gap and open the first path from SE schemes to images, this paper proposes a cloud assisted privacy-preserving automatic image annotation scheme, namely CAPIA. CAPIA enables cloud storage users to automatically assign keywords to their images by leveraging the power of cloud computing. Meanwhile, CAPIA prevents the cloud from learning the content of images and their keywords. Thorough analysis is carried out to demonstrate the security of CAPIA. A prototype implementation over the well-known IAPR TC-12 dataset further validates the efficiency and accuracy of CAPIA

Part-guided Relational Transformers for Fine-grained Visual Recognition

Fine-grained visual recognition is to classify objects with visually similar appearances into subcategories, which has made great progress with the development of deep CNNs. However, handling subtle differences between different subcategories still remains a challenge. In this paper, we propose to solve this issue in one unified framework from two aspects, i.e., constructing feature-level interrelationships, and capturing part-level discriminative features. This framework, namely PArt-guided Relational Transformers (PART), is proposed to learn the discriminative part features with an automatic part discovery module, and to explore the intrinsic correlations with a feature transformation module by adapting the Transformer models from the field of natural language processing. The part discovery module efficiently discovers the discriminative regions which are highly-corresponded to the gradient descent procedure. Then the second feature transformation module builds correlations within the global embedding and multiple part embedding, enhancing spatial interactions among semantic pixels. Moreover, our proposed approach does not rely on additional part branches in the inference time and reaches state-of-the-art performance on 3 widely-used fine-grained object recognition benchmarks. Experimental results and explainable visualizations demonstrate the effectiveness of our proposed approach. The code can be found at https://github.com/iCVTEAM/PART.Comment: Published in IEEE TIP 202

Modeling redistribution of α-HCH in Chinese soil induced by environment factors

This study explores long-term environmental fate of alpha-HCH in China from 1952 to 2007 using ChnGPERM (Chinese Gridded Pesticide Emission and Residue Model). The model captures well the temporal and spatial variations of alpha-HCH concentration in Chinese soils by comparing with a number of measured data across China in different periods. The results demonstrate alpha-HCH grasshopping effect in Eastern China and reveal several important features of the chemical in Northeast and Southeast China. It is found that Northeast China is a prominent sink region of alpha-HCH emitted from Chinese sources and alpha-HCH contamination in Southwest China is largely attributed to foreign sources. Southeast China is shown to be a major source contributing to alpha-HCH contamination in Northeast China, incurred by several environmental factors including temperature, soil organic carbon content, wind field and precipitation. (C) 2011 Elsevier Ltd. All rights reserved.This study explores long-term environmental fate of alpha-HCH in China from 1952 to 2007 using ChnGPERM (Chinese Gridded Pesticide Emission and Residue Model). The model captures well the temporal and spatial variations of alpha-HCH concentration in Chinese soils by comparing with a number of measured data across China in different periods. The results demonstrate alpha-HCH grasshopping effect in Eastern China and reveal several important features of the chemical in Northeast and Southeast China. It is found that Northeast China is a prominent sink region of alpha-HCH emitted from Chinese sources and alpha-HCH contamination in Southwest China is largely attributed to foreign sources. Southeast China is shown to be a major source contributing to alpha-HCH contamination in Northeast China, incurred by several environmental factors including temperature, soil organic carbon content, wind field and precipitation. (C) 2011 Elsevier Ltd. All rights reserved