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Robust video steganography for social media sharing based on principal component analysis 基于主成分分析的社交媒体共享鲁棒视频隐写
IF 3.6 Q1 Computer Science Pub Date : 2021-12-29 DOI: 10.21203/rs.3.rs-1109366/v1
Pingan Fan, Hong Zhang, Xianfeng Zhao
Most social media channels are lossy where videos are transcoded to reduce transmission bandwidth or storage space, such as social networking sites and video sharing platforms. Video transcoding makes most video steganographic schemes unusable for hidden communication based on social media. This paper proposes robust video steganography against video transcoding to construct reliable hidden communication on social media channels. A new strategy based on principal component analysis is provided to select robust embedding regions. Besides, side information is generated to label these selected regions. Side information compression is designed to reduce the transmission bandwidth cost. Then, one luminance component and one chrominance component are joined to embed secret messages and side information, notifying the receiver of correct extraction positions. Video preprocessing is conducted to improve the applicability of our proposed method to various video transcoding mechanisms. Experimental results have shown that our proposed method provides stronger robustness against video transcoding than other methods and achieves satisfactory security performance against steganalysis. Compared with some existing methods, our proposed method is more robust and reliable to realize hidden communication over social media channels, such as YouTube and Vimeo.
大多数社交媒体渠道都是有损的,其中视频被转编码以减少传输带宽或存储空间,例如社交网站和视频共享平台。视频转码使得大多数视频隐写方案无法用于基于社交媒体的隐藏通信。本文提出了针对视频转码的鲁棒视频隐写技术,以构建可靠的社交媒体渠道隐藏通信。提出了一种基于主成分分析的鲁棒嵌入区域选择策略。此外,生成侧信息来标记这些选定的区域。侧信息压缩是为了降低传输带宽成本而设计的。然后,将一个亮度分量和一个色度分量连接以嵌入秘密消息和侧信息,通知接收方正确的提取位置。为了提高我们提出的方法对各种视频转码机制的适用性,对视频进行了预处理。实验结果表明,该方法对视频转码具有较强的鲁棒性,对隐写分析具有较好的安全性能。与现有的一些方法相比,我们提出的方法在实现YouTube和Vimeo等社交媒体渠道上的隐藏通信方面具有更强的鲁棒性和可靠性。
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引用次数: 1
Detection of illicit cryptomining using network metadata 使用网络元数据检测非法加密挖掘
IF 3.6 Q1 Computer Science Pub Date : 2021-12-01 DOI: 10.1186/s13635-021-00126-1
Michele Russo, Nedim Srndic, P. Laskov
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引用次数: 3
Multitask adversarial attack with dispersion amplification 分散放大的多任务对抗攻击
IF 3.6 Q1 Computer Science Pub Date : 2021-09-22 DOI: 10.1186/s13635-021-00124-3
P. Haleta, Dmytro Likhomanov, Oleksandra Sokol
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引用次数: 3
Potential advantages and limitations of using information fusion in media forensics—a discussion on the example of detecting face morphing attacks 在媒体取证中使用信息融合的潜在优势和局限性——以人脸变形攻击检测为例的讨论
IF 3.6 Q1 Computer Science Pub Date : 2021-07-29 DOI: 10.1186/s13635-021-00123-4
Christian Kraetzer, A. Makrushin, J. Dittmann, M. Hildebrandt
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引用次数: 3
Detection of illicit cryptomining using network metadata 使用网络元数据检测非法加密挖掘
IF 3.6 Q1 Computer Science Pub Date : 2021-07-01 DOI: 10.21203/RS.3.RS-607598/V1
M. Russo, Nedim Srndic, P. Laskov
Illicit cryptocurrency mining has become one of the prevalent methods for monetization of computer security incidents. In this attack, victims’ computing resources are abused to mine cryptocurrency for the benefit of attackers. The most popular illicitly mined digital coin is Monero as it provides strong anonymity and is efficiently mined on CPUs.Illicit mining crucially relies on communication between compromised systems and remote mining pools using the de facto standard protocol Stratum. While prior research primarily focused on endpoint-based detection of in-browser mining, in this paper, we address network-based detection of cryptomining malware in general. We propose XMR-Ray, a machine learning detector using novel features based on reconstructing the Stratum protocol from raw NetFlow records. Our detector is trained offline using only mining traffic and does not require privacy-sensitive normal network traffic, which facilitates its adoption and integration.In our experiments, XMR-Ray attained 98.94% detection rate at 0.05% false alarm rate, outperforming the closest competitor. Our evaluation furthermore demonstrates that it reliably detects previously unseen mining pools, is robust against common obfuscation techniques such as encryption and proxies, and is applicable to mining in the browser or by compiled binaries. Finally, by deploying our detector in a large university network, we show its effectiveness in protecting real-world systems.
非法加密货币挖掘已经成为计算机安全事件货币化的普遍方法之一。在这种攻击中,受害者的计算资源被滥用来挖掘加密货币,从而使攻击者受益。最受欢迎的非法开采数字货币是门罗币,因为它提供了强大的匿名性,并且在cpu上有效地开采。非法挖矿主要依赖于受损系统和远程矿池之间使用事实上的标准协议Stratum的通信。虽然之前的研究主要集中在基于端点的浏览器内挖掘检测上,但在本文中,我们一般讨论基于网络的加密挖掘恶意软件检测。我们提出XMR-Ray,一种机器学习检测器,使用基于原始NetFlow记录重建地层协议的新特征。我们的检测器仅使用挖掘流量进行离线训练,不需要隐私敏感的正常网络流量,这有助于其采用和集成。在我们的实验中,XMR-Ray的检测率达到98.94%,虚警率为0.05%,优于最接近的竞争对手。我们的评估进一步表明,它可靠地检测以前未见过的矿池,对加密和代理等常见混淆技术具有鲁棒性,并且适用于在浏览器中或通过编译的二进制文件进行挖掘。最后,通过在大型大学网络中部署我们的检测器,我们展示了它在保护现实世界系统方面的有效性。
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引用次数: 6
Estimating Previous Quantization Factors on Multiple JPEG Compressed Images 多JPEG压缩图像前量化因子估计
IF 3.6 Q1 Computer Science Pub Date : 2021-06-28 DOI: 10.1186/s13635-021-00120-7
Sebastiano Battiato, Oliver Giudice, Francesco Guarnera, Giovanni Puglisi
The JPEG compression algorithm has proven to be efficient in saving storage and preserving image quality thus becoming extremely popular. On the other hand, the overall process leaves traces into encoded signals which are typically exploited for forensic purposes: for instance, the compression parameters of the acquisition device (or editing software) could be inferred. To this aim, in this paper a novel technique to estimate “previous” JPEG quantization factors on images compressed multiple times, in the aligned case by analyzing statistical traces hidden on Discrete Cosine Transform (DCT) histograms is exploited. Experimental results on double, triple and quadruple compressed images, demonstrate the effectiveness of the proposed technique while unveiling further interesting insights.
JPEG压缩算法已被证明在节省存储空间和保持图像质量方面非常有效,因此非常受欢迎。另一方面,整个过程在编码信号中留下痕迹,这些信号通常被用于取证目的:例如,可以推断出采集设备(或编辑软件)的压缩参数。为此,本文提出了一种新的技术,通过分析隐藏在离散余弦变换(DCT)直方图上的统计痕迹,对多次压缩后的图像估计“先前”JPEG量化因子。双压缩、三压缩和四压缩图像的实验结果证明了所提出技术的有效性,同时揭示了进一步有趣的见解。
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引用次数: 4
Proposing a reliable method of securing and verifying the credentials of graduates through blockchain 提出一种通过区块链保护和验证毕业生证书的可靠方法
IF 3.6 Q1 Computer Science Pub Date : 2021-06-26 DOI: 10.1186/s13635-021-00122-5
T. Rama Reddy, P. V. G. D. Prasad Reddy, Rayudu Srinivas, Ch. V. Raghavendran, R. V. S. Lalitha, B. Annapurna
Education acts as a soul in the overall societal development, in one way or the other. Aspirants, who gain their degrees genuinely, will help society with their knowledge and skills. But, on the other side of the coin, the problem of fake certificates is alarming and worrying. It has been prevalent in different forms from paper-based dummy certificates to replicas backed with database tampering and has increased to astronomic levels in this digital era. In this regard, an overlay mechanism using blockchain technology is proposed to store the genuine certificates in digital form and verify them firmly whenever needed without delay. The proposed system makes sure that the certificates, once verified, can be present online in an immutable form for further reference and provides a tamper-proof concealment to the existing certification system. To confirm the credibility of the proposed method, a prototype of blockchain-based credential securing and verification system is developed in ethereum test network. The implementation and test results show that it is a secure and feasible solution to online credential management system.
教育以这样或那样的方式在整个社会发展中扮演着灵魂的角色。真正获得学位的有志者将用他们的知识和技能帮助社会。但是,在硬币的另一面,假证书的问题令人担忧和担忧。它以不同的形式流行,从基于纸张的虚拟证书到数据库篡改的副本,并在这个数字时代增加到天文数字的水平。为此,提出了一种使用区块链技术的覆盖机制,以数字形式存储真实证书,并在需要时进行可靠的验证,而不会延迟。所提议的系统确保证书在经过验证后,可以以不可变的形式在线呈现,以供进一步参考,并为现有的认证系统提供防篡改的隐藏。为了验证所提方法的可信性,在以太坊测试网络中开发了一个基于区块链的证书保护与验证系统原型。实现和测试结果表明,该系统是一种安全可行的在线证书管理系统解决方案。
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引用次数: 12
On the information leakage quantification of camera fingerprint estimates 相机指纹估计的信息泄漏量化研究
IF 3.6 Q1 Computer Science Pub Date : 2021-06-03 DOI: 10.1186/s13635-021-00121-6
Samuel Fernández-Menduiña, Fernando Pérez-González
Camera fingerprints based on sensor PhotoResponse Non-Uniformity (PRNU) have gained broad popularity in forensic applications due to their ability to univocally identify the camera that captured a certain image. The fingerprint of a given sensor is extracted through some estimation method that requires a few images known to be taken with such sensor. In this paper, we show that the fingerprints extracted in this way leak a considerable amount of information from those images used in the estimation, thus constituting a potential threat to privacy. We propose to quantify the leakage via two measures: one based on the Mutual Information, and another based on the output of a membership inference test. Experiments with practical fingerprint estimators on a real-world image dataset confirm the validity of our measures and highlight the seriousness of the leakage and the importance of implementing techniques to mitigate it. Some of these techniques are presented and briefly discussed.
基于传感器光响应非均匀性(PRNU)的相机指纹在法医应用中获得了广泛的普及,因为它们能够唯一地识别拍摄特定图像的相机。通过某种估计方法提取给定传感器的指纹,该估计方法需要使用该传感器拍摄少量已知图像。在本文中,我们证明了以这种方式提取的指纹泄露了用于估计的图像中的大量信息,从而构成了对隐私的潜在威胁。我们建议通过两个度量来量化泄漏:一个基于互信息,另一个基于隶属推理测试的输出。在真实图像数据集上使用实际指纹估计器进行的实验证实了我们的措施的有效性,并强调了泄漏的严重性以及实施减轻泄漏的技术的重要性。本文介绍并简要讨论了其中的一些技术。
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引用次数: 4
Media forensics on social media platforms: a survey 社交媒体平台的媒体取证:一项调查
IF 3.6 Q1 Computer Science Pub Date : 2021-05-01 DOI: 10.1186/s13635-021-00117-2
Cecilia Pasquini, Irene Amerini, Giulia Boato
The dependability of visual information on the web and the authenticity of digital media appearing virally in social media platforms has been raising unprecedented concerns. As a result, in the last years the multimedia forensics research community pursued the ambition to scale the forensic analysis to real-world web-based open systems. This survey aims at describing the work done so far on the analysis of shared data, covering three main aspects: forensics techniques performing source identification and integrity verification on media uploaded on social networks, platform provenance analysis allowing to identify sharing platforms, and multimedia verification algorithms assessing the credibility of media objects in relation to its associated textual information. The achieved results are highlighted together with current open issues and research challenges to be addressed in order to advance the field in the next future.
网络上视觉信息的可靠性和社交媒体平台上病毒式传播的数字媒体的真实性引发了前所未有的关注。因此,在过去的几年中,多媒体取证研究社区追求将取证分析扩展到现实世界中基于web的开放系统的目标。本调查旨在描述迄今为止在共享数据分析方面所做的工作,涵盖三个主要方面:对社交网络上上传的媒体进行来源识别和完整性验证的取证技术,允许识别共享平台的平台来源分析,以及评估与其相关文本信息相关的媒体对象可信度的多媒体验证算法。已取得的成果与当前开放的问题和需要解决的研究挑战一起突出显示,以便在未来推进该领域。
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引用次数: 31
Research on gray correlation analysis and situation prediction of network information security 网络信息安全的灰色关联分析与态势预测研究
IF 3.6 Q1 Computer Science Pub Date : 2021-04-20 DOI: 10.1186/s13635-021-00118-1
Chen Ye, Wenyu Shi, Rui Zhang
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引用次数: 5
期刊
EURASIP Journal on Information Security
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