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Proceedings of the 29th USENIX Conference on security Symposium

作者： Shuitao Gan Chao Zhang Peng Chen Bodong Zhao Xiaojun Qin Dong Wu Zuoning Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Institute for Network Science and Cyberspace Tsinghua University and Beijing National Research Center for Information Science and Technology ByteDance AI lab Institute for Network Science and Cyberspace Tsinghua University National Research Center of Parallel Computer Engineering and Technology

ISBN: (纸本)9781939133175

Data flow analysis (e.g., dynamic taint analysis) has proven to be useful for guiding fuzzers to explore hard-to-reach code and find vulnerabilities. However, traditional taint analysis is labor-intensive, inaccurate and slow, affecting the fuzzing efficiency. Apart from taint, few data flow features are *** this paper, we proposed a data flow sensitive fuzzing solution GREYONE. We first utilize the classic feature taint to guide fuzzing. A lightweight and sound fuzzing-driven taint inference (FTI) is adopted to infer taint of variables, by monitoring their value changes while mutating input bytes during fuzzing. With the taint, we propose a novel input prioritization model to determine which branch to explore, which bytes to mutate and how to mutate. Further, we use another data flow feature constraint conformance, i.e., distance of tainted variables to values expected in untouched branches, to tune the evolution direction of *** implemented a prototype of GREYONE and evaluated it on the LAVA data set and 19 real world programs. The results showed that it outperforms various state-of-the-art fuzzers in terms of both code coverage and vulnerability discovery. In the LAVA data set, GREYONE found all listed bugs and 336 more unlisted. In real world programs, GREYONE on average found 2.12X unique program paths and 3.09X unique bugs than state-of-the-art evolutionary fuzzers, including AFL, VUzzer, CollAFL, Angora and Honggfuzz, Moreover, GREYONE on average found 1.2X unique program paths and 1.52X unique bugs than a state-of-the-art symbolic exeuction assisted fuzzer QSYM. In total, it found 105 new security bugs, of which 41 are confirmed by CVE.

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Transformation driven visual reasoning

arXiv

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arXiv 2020年

作者： Hong, Xin Lan, Yanyan Pang, Liang Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute for AI Industry Research Tsinghua University Beijing China

This paper defines a new visual reasoning paradigm by introducing an important factor, i.e. transformation. The motivation comes from the fact that most existing visual reasoning tasks, such as CLEVR in VQA, are solely defined to test how well the machine understands the concepts and relations within static settings, like one image. We argue that this kind of state driven visual reasoning approach has limitations in reflecting whether the machine has the ability to infer the dynamics between different states, which has been shown as important as state-level reasoning for human cognition in Piaget’s theory. To tackle this problem, we propose a novel transformation driven visual reasoning task. Given both the initial and final states, the target is to infer the corresponding single-step or multi-step transformation, represented as a triplet (object, attribute, value) or a sequence of triplets, respectively. Following this definition, a new dataset namely TRANCE is constructed on the basis of CLEVR, including three levels of settings, i.e. Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Experimental results show that the state-of-the-art visual reasoning models perform well on Basic, but are still far from human-level intelligence on Event and View. We believe the proposed new paradigm will boost the development of machine visual reasoning. More advanced methods and real data need to be investigated in this direction. The resource of TVR is available at https://***/TVR. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Design of Target Behavior Intelligent Analysis System Based on Distributed computing

Design of Target Behavior Intelligent Analysis System Based ...

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2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019

作者： Pan, Xinlong Cheng, Xueqi Yao, Libo Zhu, Zhenqiu DIng, Biao Tang, Tiantian Li, Minbo Liu, Chuanhui Institute of Information Fusion Naval Aviation University Yantai China CAS Key Laboratory of Network Data Science Technology Inistitute of Computing Technology Chinese Academy of Sciences Beijing China School of Software Fudan University Shanghai China

ISBN: (纸本)9781728123455

Through the fusion processing of target data obtained by radar, electronic reconnaissance, space reconnaissance, technical reconnaissance and civil cooperative system in early warning and surveillance system, multidimensional target data including explicit state information such as target attributes, type, position, speed and course could be obtained. The latent potential information, such as regular behavior, intention and threat degree, need to be mastered by human intelligence and experience analysis. It is urgent to use big data mining technology to realize intelligent analysis of target behavior. Based on the analysis of big data related technologies, this paper designs an intelligent target behavior analysis system based on distributed computing, which provides theoretical and technical support for intelligent upgrading of intelligence processing systems and command decision systems. © 2019 IEEE.

关键词： Data mining

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Mobile Robot Object Recognition in The Internet of Things based on Fog computing

Mobile Robot Object Recognition in The Internet of Things ba...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Meixia Fu Songlin Sun Kaili Ni Xiaoying Hou National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service (BUPT)

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Mobile robot object recognition has attracted significant attention in the internet of things recently, in which there are many challenging tasks, such as the objects, the communication networks and the computer system. It still needs a large of computation, communication and storage capability for the whole system. In this paper, we propose a scheme of mobile robot object recognition in IOT and use edge nodes to process the data from robot vision instead of cloud computing. Besides, we adopt YOLOv3 as the main algorithm in the edge nodes to process the video data. The video from the camera on the robot is transmitted to the fog node by a Wi-Fi router. The advantages of using edge nodes for computation local robot clusters are reliability, real-time capability and flexibility compared with the cloud. In our experiment, we efficiently train the computer model using COCO database deployed to GPU. The robot using the proposed model could recognize the objects in real-time. We achieve mAP of 31.0% and response time of 52ms that illustrates the state-of-art performance of the proposed framework.

关键词： Object recognition Edge computing Cloud computing Real-time systems Servers Mobile robots

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Multi-modal datasets for super-resolution

arXiv

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arXiv 2020年

作者： Li, Haoran Quan, Weihong Yan, Meijun Zhang, Jin Gong, Xiaoli Zhou, Jin College of Computer Science Nankai University Tianjin China College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Tianjin Chest Hospital China

Nowdays, most datasets used to train and evaluate super-resolution models are single-modal simulation datasets. However, due to the variety of image degradation types in the real world, models trained on single-modal simulation datasets do not always have good robustness and generalization ability in different degradation scenarios. Previous work tended to focus only on true-color images. In contrast, we first proposed real-world black-and-white old photo datasets for super-resolution (OID-RW), which is constructed using two methods of manually filling pixels and shooting with different cameras. The dataset contains 82 groups of images, including 22 groups of character type and 60 groups of landscape and architecture. At the same time, we also propose a multi-modal degradation dataset (MDD400) to solve the super-resolution reconstruction in real-life image degradation scenarios. We managed to simulate the process of generating degraded images by the following four methods: interpolation algorithm, CNN network, GAN network and capturing videos with different bit rates. Our experiments demonstrate that not only the models trained on our dataset have better generalization capability and robustness, but also the trained images can maintain better edge contours and texture features. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Secure Data Propagation in Edge-Cloud Orchestrated computing Paradigms: Statistcs and Taxonomy

Secure Data Propagation in Edge-Cloud Orchestrated Computing...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Feng Yu Xixian Li Huimin Zhang Li-e Wang College of computer science Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China Guangxi Key Lab of Multi-source Information Mining &Security Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

Despite the importance of the secure data propagation in the edge-cloud orchestrated computing paradigms, to the best of our knowledge, there is lack of systematic and comprehensive review on this research topic. The purpose of this article is to provide a systematic literature survey on secure data propagation in edge-cloud orchestrated computing environment to examine the current technical achievements published between 2010 and February 2020. The brief contributions of the paper are as follows: (1)providing a comprehensive literature review of the secure data propagation approaches in the edge-cloud orchestrated computing paradigms, (2) designing a methodology for document retrieval and selection, (3) presenting analysis and comparison for main technique methods of secure data propagation in hybrid edge-cloud environment.

关键词： Systematics Bibliographies Taxonomy Big Data

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A Control Task Assignment Algorithm based on sub-graph isomorphism

A Control Task Assignment Algorithm based on sub-graph isomo...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Feng Yu Xixian Li Huimin Zhang Li-e Wang College of computer science Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

To enhance the controllability and trustworthiness of the Internet, a new network control architecture has been presented with control task extracted to the intra-domain control node and executed in distributed multi-domain cooperative style. The study on control task assigning approach plays a central role in this network control pattern and shares some features with virtual network mapping problem. Different from the previous heuristics-based algorithms, we designs a baseline assigning algorithm which tests if the control task request can be mapped to a sub-graph of substrate control network or not without control task migration. Subsequently, an extend version is described under task migration considered. Our experiment evaluations show the impact of parameters on the runtime and assigning accuracy rate.

关键词： Cloud computing Backtracking Runtime Heuristic algorithms Process control Big Data Feature extraction

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DeeperForensics challenge 2020 on real-world face forgery detection: Methods and results

arXiv

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arXiv 2021年

作者： Jiang, Liming Guo, Zhengkui Wu, Wayne Liu, Zhaoyang Liu, Ziwei Loy, Chen Change Yang, Shuo Xiong, Yuanjun Xia, Wei Chen, Baoying Zhuang, Peiyu Li, Sili Chen, Shen Yao, Taiping Ding, Shouhong Li, Jilin Huang, Feiyue Cao, Liujuan Ji, Rongrong Lu, Changlei Tan, Ganchao S-Lab Nanyang Technological University Singapore Chinese University Hong Kong SenseTime Research Hong Kong Amazon Web Services. United States Shenzhen Key Laboratory of Media Information Content Security Shenzhen University China Media Analytics and Computing Lab Xiamen University China YouTu Lab Tencent Shanghai China University of Science and Technology China

This paper reports methods and results in the DeeperForensics Challenge 2020 on real-world face forgery detection. The challenge employs the DeeperForensics-1.0 dataset, one of the most extensive publicly available real-world face forgery detection datasets, with 60,000 videos constituted by a total of 17.6 million frames. The model evaluation is conducted online on a high-quality hidden test set with multiple sources and diverse distortions. A total of 115 participants registered for the competition, and 25 teams made valid submissions. We will summarize the winning solutions and present some discussions on potential research directions. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Generating superpixels for high-resolution images with Decoupled Patch Calibration

arXiv

引用

arXiv 2021年

作者： Wang, Yaxiong Wei, Yunchao Qian, Xueming Zhu, Li Yang, Yi The School of Software Engineering Xi'an Jiaotong University Xi'an710049 China School of Computer and Information Technology Beijing Jiaotong University Beijing100000 China The Key Laboratory for Intelligent Networks and Network Security Ministry of Education Xi'an Jiaotong University Xi'an710049 China The SMILES Laboratory Xi'an Jiaotong University Xi'an710049 China Zhibian Technology Co. Ltd. Taizhou317000 China The School of Software Xi'an Jiaotong University Xi'an710049 China School of Computer Science and Technology Zhejiang University Hangzhou310000 China

Superpixel segmentation has recently seen important progress benefiting from the advances in differentiable deep learning. However, the very high-resolution superpixel segmentation still remains challenging due to the expensive memory and computation cost, making the current advanced superpixel networks fail to process. In this paper, we devise Patch Calibration networks (PCNet), aiming to efficiently and accurately implement high-resolution superpixel segmentation. PCNet follows the principle of producing high-resolution output from low-resolution input for saving GPU memory and relieving computation cost. To recall the fine details destroyed by the down-sampling operation, we propose a novel Decoupled Patch Calibration (DPC) branch for collaboratively augment the main superpixel generation branch. In particular, DPC takes a local patch from the high-resolution images and dynamically generates a binary mask to impose the network to focus on region boundaries. By sharing the parameters of DPC and main branches, the fine-detailed knowledge learned from high-resolution patches will be transferred to help calibrate the destroyed information. To the best of our knowledge, we make the first attempt to consider the deep-learning-based superpixel generation for high-resolution cases. To facilitate this research, we build evaluation benchmarks from two public datasets and one new constructed one, covering a wide range of diversities from fine-grained human parts to cityscapes. Extensive experiments demonstrate that our PCNet can not only perform favorably against the state-of-the-arts in the quantitative results but also improve the resolution upper bound from 3K to 5K on 1080Ti GPUs. © 2021, CC BY.

关键词： Image segmentation

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Modeling the Correlations of Relations for Knowledge Graph Embedding

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Journal of Computer Science & technology 2018年第2期33卷 323-334页

作者： Ji-Zhao Zhu Yan-Tao Jia Jun Xu Jian-Zhong Qiao Xue-Qi Cheng College of Computer Science and Engineering Northeastern University Shenyang 110169 China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing 110190 China

Knowledge graph embedding, which maps the entities and relations into low-dimensional vector spaces, has demonstrated its effectiveness in many tasks such as link prediction and relation extraction. Typical methods include TransE, TransH, and TransR. All these methods map different relations into the vector space separately and the intrinsic correlations of these relations are ignored. It is obvious that there exist some correlations among relations because different relations may connect to a common entity. For example, the triples （Steve Jobs, PlaceOfBrith, California） and （Apple Inc., Location, California） share the same entity California as their tail entity. We analyze the embedded relation matrices learned by TransE/TransH/TransR, and find that the correlations of relations do exist and they are showed as low-rank structure over the embedded relation matrix. It is natural to ask whether we can leverage these correlations to learn better embeddings for the entities and relations in a knowledge graph. In this paper, we propose to learn the embedded relation matrix by decomposing it as a product of two low-dimensional matrices, for characterizing the low-rank structure. The proposed method, called TransCoRe （Translation-Based Method via Modeling the Correlations of Relations）, learns the embeddings of entities and relations with translation-based framework. Experimental results based on the benchmark datasets of WordNet and Freebase demonstrate that our method outperforms the typical baselines on link prediction and triple classification tasks.

关键词： knowledge graph embedding low-rank matrix decomposition

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