2014 IEEE International Workshop on Information Forensics and Security (WIFS)最新文献

Bidimensional empirical mode decomposition-based unlighting for face recognition 基于二维经验模态分解的消光人脸识别

2014 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2014-12-01 DOI: 10.1109/WIFS.2014.7084297

Miguel A. Ochoa-Villegas, J. Nolazco-Flores, Olivia Barron-Cano, I. Kakadiaris

引用次数: 1

FiberID: molecular-level secret for identification of things FiberID:用于识别事物的分子级秘密

2014 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2014-12-01 DOI: 10.1109/WIFS.2014.7084308

Zhen Chen, Yongbo Zeng, Gerald Hefferman, Y. Sun

引用次数: 26

Understanding the effects of real-world behavior in statistical disclosure attacks 了解统计披露攻击中真实世界行为的影响

2014 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2014-12-01 DOI: 10.1109/WIFS.2014.7084306

Simon Oya, C. Troncoso, F. Pérez-González

引用次数: 2

A feature-based approach for image tampering detection and localization 基于特征的图像篡改检测与定位方法

2014 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2014-12-01 DOI: 10.1109/WIFS.2014.7084319

L. Verdoliva, D. Cozzolino, G. Poggi

引用次数: 71

Face recognition via adaptive sparse representations of random patches 基于随机斑块自适应稀疏表示的人脸识别

2014 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2014-12-01 DOI: 10.1109/WIFS.2014.7084296

D. Mery, K. Bowyer

{"title":"Face recognition via adaptive sparse representations of random patches","authors":"D. Mery, K. Bowyer","doi":"10.1109/WIFS.2014.7084296","DOIUrl":"https://doi.org/10.1109/WIFS.2014.7084296","url":null,"abstract":"Unconstrained face recognition is still an open problem, as state-of-the-art algorithms have not yet reached high recognition performance in real-world environments (e.g., crowd scenes at the Boston Marathon). This paper addresses this problem by proposing a new approach called Adaptive Sparse Representation of Random Patches (ASR+). In the learning stage, for each enrolled subject, a number of random patches are extracted from the subject's gallery images in order to construct representative dictionaries. In the testing stage, random test patches of the query image are extracted, and for each test patch a dictionary is built concatenating the `best' representative dictionary of each subject. Using this adapted dictionary, each test patch is classified following the Sparse Representation Classification (SRC) methodology. Finally, the query image is classified by patch voting. Thus, our approach is able to deal with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size and distance from the camera. Experiments were carried out on five widely-used face databases. Results show that ASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature in many complex scenarios.","PeriodicalId":220523,"journal":{"name":"2014 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134070250","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: 16

Malware detection using HTTP user-agent discrepancy identification 使用HTTP用户代理差异识别进行恶意软件检测

2014 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2014-12-01 DOI: 10.1109/WIFS.2014.7084331

Martin Grill, M. Rehák

{"title":"Malware detection using HTTP user-agent discrepancy identification","authors":"Martin Grill, M. Rehák","doi":"10.1109/WIFS.2014.7084331","DOIUrl":"https://doi.org/10.1109/WIFS.2014.7084331","url":null,"abstract":"Botnet detection systems that use Network Behavioral Analysis (NBA) principle struggle with performance and privacy issues on large-scale networks. Because of that many researchers focus on fast and simple bot detection methods that at the same time use as little information as possible to avoid privacy violations. Next, deep inspections, reverse engineering, clustering and other time consuming approaches are typically unfeasible in large-scale networks. In this paper we present a novel technique that uses User- Agent field contained in the HTTP header, that can be easily obtained from the web proxy logs, to identify malware that uses User-Agents discrepant with the ones actually used by the infected user. We are using statistical information about the usage of the User-Agent of each user together with the usage of particular User-Agent across the whole analyzed network and typically visited domains. Using those statistics we can identify anomalies, which we proved to be caused by malware-infected hosts in the network. Because of our simple and computationally inexpensive approach we can inspect data from extremely large networks with minimal computational costs.","PeriodicalId":220523,"journal":{"name":"2014 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123945067","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: 28

Unsupervised feature learning for bootleg detection using deep learning architectures 使用深度学习架构进行盗版检测的无监督特征学习

2014 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2014-12-01 DOI: 10.1109/WIFS.2014.7084316

Michele Buccoli, Paolo Bestagini, M. Zanoni, A. Sarti, S. Tubaro

{"title":"Unsupervised feature learning for bootleg detection using deep learning architectures","authors":"Michele Buccoli, Paolo Bestagini, M. Zanoni, A. Sarti, S. Tubaro","doi":"10.1109/WIFS.2014.7084316","DOIUrl":"https://doi.org/10.1109/WIFS.2014.7084316","url":null,"abstract":"The widespread diffusion of portable devices capable of capturing high-quality multimedia data, together with the rapid proliferation of media sharing platforms, has determined an incredible growth of user-generated content available online. Since it is hard to strictly regulate this trend, illegal diffusion of copyrighted material is often likely to occur. This is the case of audio bootlegs, i.e., concerts illegally recorded and redistributed by fans. In this paper, we propose a bootleg detector, with the aim of disambiguating between: i) bootlegs unofficially recorded; ii) live concerts officially published; iii) studio recordings from officially released albums. The proposed method is based on audio feature analysis and machine learning techniques. We exploit a deep learning paradigm to extract highly characterizing features from audio excerpts, and a supervised classifier for detection. The method is validated against a dataset of nearly 500 songs, and results are compared to a state-of-the-art detector. The conducted experiments confirm the capability of deep learning techniques to outperform classic feature extraction approaches.","PeriodicalId":220523,"journal":{"name":"2014 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124096498","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: 23

The optimal attack to histogram-based forensic detectors is simple(x) 对基于直方图的取证检测器的最佳攻击很简单(x)

2014 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2014-12-01 DOI: 10.1109/WIFS.2014.7084317

Pedro Comesaña Alfaro, F. Pérez-González

引用次数: 13

Minutiae set to bit-string conversion using multi-scale bag-of-words paradigm 细枝末节设置为使用多尺度词袋范式的位串转换

2014 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2014-12-01 DOI: 10.1109/WIFS.2014.7084294

W. Wong, M. D. Wong, Y. Kho, A. Teoh

引用次数: 5

Selection-channel-aware rich model for Steganalysis of digital images 数字图像隐写分析的选择通道感知丰富模型

2014 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2014-12-01 DOI: 10.1109/WIFS.2014.7084302

Tomáš Denemark, V. Sedighi, Vojtech Holub, R. Cogranne, J. Fridrich

引用次数: 293