2017 IEEE Workshop on Information Forensics and Security (WIFS)最新文献_第2页

A fault-tolerant and efficient scheme for data aggregation over groups in the smart grid 智能电网组间数据聚合的一种容错高效方案

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267646

F. Knirsch, D. Engel, Z. Erkin

引用次数: 9

Robust secure storage of data sources with perfect secrecy 具有完美保密性的数据源的可靠安全存储

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267669

Sebastian Baur, H. Boche

引用次数: 3

Tracing images back to their social network of origin: A CNN-based approach 追踪图像回到它们的社交网络来源:一种基于cnn的方法

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267660

Irene Amerini, Tiberio Uricchio, R. Caldelli

{"title":"Tracing images back to their social network of origin: A CNN-based approach","authors":"Irene Amerini, Tiberio Uricchio, R. Caldelli","doi":"10.1109/WIFS.2017.8267660","DOIUrl":"https://doi.org/10.1109/WIFS.2017.8267660","url":null,"abstract":"Recovering information about the history of a digital content, such as an image or a video, can be strategic to address an investigation from the early stages. Storage devices, smart-phones and PCs, belonging to a suspect, are usually confiscated as soon as a warrant is issued. Any multimedia content found is analyzed in depth, in order to trace back its provenance and, if possible, its original source. This is particularly important when dealing with social networks, where most of the user-generated photos and videos are uploaded and shared daily. Being able to discern if images are downloaded from a social network or directly captured by a digital camera, can be crucial in leading consecutive investigations. In this paper, we propose a novel method based on convolutional neural networks (CNN) to determine the image provenance, whether it originates from a social network, a messaging application or directly from a photo-camera. By considering only the visual content, the method works irrespective of an eventual manipulation of metadata performed by an attacker. We have tested the proposed technique on three publicly available datasets of images downloaded from seven popular social networks, obtaining state-of-the-art results.","PeriodicalId":305837,"journal":{"name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126470185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 37

Benchmarking keystroke authentication algorithms 基准击键认证算法

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267670

Jiaju Huang, Daqing Hou, S. Schuckers, Timothy Law, Adam Sherwin

{"title":"Benchmarking keystroke authentication algorithms","authors":"Jiaju Huang, Daqing Hou, S. Schuckers, Timothy Law, Adam Sherwin","doi":"10.1109/WIFS.2017.8267670","DOIUrl":"https://doi.org/10.1109/WIFS.2017.8267670","url":null,"abstract":"Free-text keystroke dynamics is a behavioral biometric that has the strong potential to offer unobtrusive and continuous user authentication. Such behavioral biometrics are important as they may serve as an additional layer of protection over other one-stop authentication methods such as the user ID and passwords. Unfortunately, evaluation and comparison of keystroke dynamics algorithms are still lacking due to the absence of large, shared free-text datasets. In this research, we present a novel keystroke dynamics algorithm, based on kernel density estimation (KDE), and contrast it with two other state-of-the-art algorithms, namely Gunetti & Picardi's and Buffalo's SVM algorithms, using three published datasets, as well as our own new, unconstrained dataset that is an order of magnitude larger than the previous ones. We modify the algorithms when necessary such that they have comparable settings, including profile and test sample sizes. Both Gunetti & Picardi's and our own KDE algorithms have performed much better than Buffalo's SVM algorithm. Although much simpler, the newly developed KDE algorithm is shown to perform similarly as Gunetti & Picardi's algorithm on the three constrained datasets, but the best on our new unconstrained dataset. All three algorithms perform significantly better on the three prior datasets, which are constrained in one way or another, than our new dataset, which is truly unconstrained. This highlights the importance of our unconstrained dataset in representing the real-world scenarios for keystroke dynamics. Lastly, the new KDE algorithm degrades the least in performance on our new dataset.","PeriodicalId":305837,"journal":{"name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132168296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Secure one-time biometrie tokens for non-repudiable multi-party transactions 为不可抵赖的多方交易提供安全的一次性生物识别令牌

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267654

K. Nandakumar, N. Ratha, Sharath Pankanti, S. Darnell

{"title":"Secure one-time biometrie tokens for non-repudiable multi-party transactions","authors":"K. Nandakumar, N. Ratha, Sharath Pankanti, S. Darnell","doi":"10.1109/WIFS.2017.8267654","DOIUrl":"https://doi.org/10.1109/WIFS.2017.8267654","url":null,"abstract":"Numerous applications require users to interact with a multitude of entities in order to avail a service. In such applications, the identity of the user is typically verified through physical or digital tokens, which are prone to both identity theft (lost or stolen tokens) and repudiation claims by malicious users. The use of biometrics can provide non-repudiability by ensuring that a purchaser is the bonafide user of the service. However, in applications entailing multiple stakeholders, there may be privacy issues with sharing user's biometrics data. While this concern can be addressed by storing the user's biometric data on the token itself, strong mechanisms are required to ensure that token is both secure and tamper-proof. In this paper, we propose a single use biometric token that relies on Shamir's secret sharing algorithm and blockchain technology to ensure that the encrypted biometric template contained in the token is secure, tamper-proof, and any attempt to use the issued token is irrefutably logged to prove subsequently that the user indeed availed the service. We also analyze issues related to the system security, user privacy, and usability of the proposed solution.","PeriodicalId":305837,"journal":{"name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127614954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Sensitivity analysis in keystroke dynamics using convolutional neural networks 基于卷积神经网络的按键动力学灵敏度分析

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267667

Hayreddin Çeker, S. Upadhyaya

引用次数: 37

Distinguishing computer graphics from natural images using convolution neural networks 使用卷积神经网络从自然图像中区分计算机图形

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267647

Nicolas Rahmouni, Vincent Nozick, J. Yamagishi, I. Echizen

引用次数: 234

Latent fingerprint segmentation based on convolutional neural networks 基于卷积神经网络的潜在指纹分割

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267655

Yanming Zhu, Xuefei Yin, X. Jia, Jiankun Hu

{"title":"Latent fingerprint segmentation based on convolutional neural networks","authors":"Yanming Zhu, Xuefei Yin, X. Jia, Jiankun Hu","doi":"10.1109/WIFS.2017.8267655","DOIUrl":"https://doi.org/10.1109/WIFS.2017.8267655","url":null,"abstract":"Latent fingerprints are important evidences used by law enforcement agencies to identify suspects for centuries. However, due to the poor image quality and complex background noise, separating the fingerprint region-of-interest from complex background is a very challenging problem. This paper proposes a new latent fingerprint segmentation method based on Convolutional Neural Networks (ConvNets). The latent fingerprint segmentation problem is formulated as a classification system, in which a set of elaborately designed ConvNets is learned to classify each patch as either fingerprint or background. Considering the spatial correlation between fingerprint patches, we proposed to train the set of ConveNets using multi-sized overlapping patches to utilize complementary information. Then, a score map is calculated based on the classification results to evaluate the possibility of a pixel belonging to the fingerprint foreground. Finally, a segmentation mask is generated by thresholding the score map and used to delineate the latent fingerprint boundary. Experimental results on NIST SD27 latent database demonstrate that the proposed method outperforms the existing benchmarks in terms of both false detection rate (FDR) and overall segmentation accuracy. Thanks to the off-line training and short segmentation running time, the proposed method is applicable to applications such as latent fingerprint matching.","PeriodicalId":305837,"journal":{"name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122882268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Design of projection matrices for PRNU compression PRNU压缩的投影矩阵设计

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267652

L. Bondi, F. Pérez-González, Paolo Bestagini, S. Tubaro

引用次数: 7

Extended beamforming by sum and difference composite co-array for radio surveillance 无线电监视用和差复合阵扩展波束形成

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267658

Sho Iwazaki, K. Ichige

引用次数: 3