Spark-based real-time proactive image tracking protection model

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

EURASIP Journal on Information Security Pub Date : 2019-04-03 DOI:10.1186/s13635-019-0086-2

Yahong Hu, Xia Sheng, Jiafa Mao, Kaihui Wang, Danhong Zhong

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引用次数: 1

Abstract

With rapid development of the Internet, images are spreading more and more quickly and widely. The phenomenon of image illegal usage emerges frequently, and this has marked impacts on people’s normal life. Therefore, it is of great importance to protect image security and image owner’s rights. At present, most image protection is passive. Most of the time, only when the images had been used illegally and serious adverse consequences had appeared did the image owners discover it. In this paper, a Spark-based real-time proactive image tracking protection model (SRPITP) is proposed to monitor the status of images under protection in real time. Whenever illegal use is found, an alert will be issued to image owners. The model mainly includes image fingerprint extraction module, image crawling module, and image matching module. The experimental results show that in SRPITP, the image matching accuracy rate is above 98.9%, and compared with its stand-alone counterpart, the corresponding time reduction for image extraction and matching are about 58.78% and 61.67%.

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基于spark的实时主动图像跟踪保护模型

随着互联网的快速发展，图像传播的速度越来越快，范围越来越广。图像非法使用现象层出不穷，对人们的正常生活造成了明显的影响。因此，保护图像安全和图像所有者的权利是非常重要的。目前，大多数图像保护是被动的。大多数情况下，只有当图像被非法使用，并出现严重的不良后果时，图像所有者才会发现。本文提出了一种基于spark的实时主动图像跟踪保护模型(SRPITP)，用于实时监测被保护图像的状态。一旦发现非法使用，将向图像所有者发出警告。该模型主要包括图像指纹提取模块、图像抓取模块和图像匹配模块。实验结果表明，在SRPITP中，图像匹配正确率达到98.9%以上，与单机相比，相应的图像提取和匹配时间分别减少了58.78%和61.67%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EURASIP Journal on Information Security COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

8.80

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： The overall goal of the EURASIP Journal on Information Security, sponsored by the European Association for Signal Processing (EURASIP), is to bring together researchers and practitioners dealing with the general field of information security, with a particular emphasis on the use of signal processing tools in adversarial environments. As such, it addresses all works whereby security is achieved through a combination of techniques from cryptography, computer security, machine learning and multimedia signal processing. Application domains lie, for example, in secure storage, retrieval and tracking of multimedia data, secure outsourcing of computations, forgery detection of multimedia data, or secure use of biometrics. The journal also welcomes survey papers that give the reader a gentle introduction to one of the topics covered as well as papers that report large-scale experimental evaluations of existing techniques. Pure cryptographic papers are outside the scope of the journal. Topics relevant to the journal include, but are not limited to: • Multimedia security primitives (such digital watermarking, perceptual hashing, multimedia authentictaion) • Steganography and Steganalysis • Fingerprinting and traitor tracing • Joint signal processing and encryption, signal processing in the encrypted domain, applied cryptography • Biometrics (fusion, multimodal biometrics, protocols, security issues) • Digital forensics • Multimedia signal processing approaches tailored towards adversarial environments • Machine learning in adversarial environments • Digital Rights Management • Network security (such as physical layer security, intrusion detection) • Hardware security, Physical Unclonable Functions • Privacy-Enhancing Technologies for multimedia data • Private data analysis, security in outsourced computations, cloud privacy