2017 IEEE Workshop on Information Forensics and Security (WIFS)最新文献_第3页

An approach for gait anonymization using deep learning 一种基于深度学习的步态匿名化方法

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267657

Ngoc-Dung T. Tieu, H. Nguyen, Hoang-Quoc Nguyen-Son, J. Yamagishi, I. Echizen

{"title":"An approach for gait anonymization using deep learning","authors":"Ngoc-Dung T. Tieu, H. Nguyen, Hoang-Quoc Nguyen-Son, J. Yamagishi, I. Echizen","doi":"10.1109/WIFS.2017.8267657","DOIUrl":"https://doi.org/10.1109/WIFS.2017.8267657","url":null,"abstract":"The human gait has become another biometrie trait used in security systems because it is unique to each person and can be recognized at a distance. However, a bad actor could use a gait recognition system to identify a person on the basis of his or her gait. We have developed a gait anonymization method that prevents unauthorized gait recognition. It modifies the gait so that the person cannot be identified while maintaining the naturalness of the gait. The modification is done by adding another gait, called \"noise gait\". A convolutional neural network makes this modification by taking two gaits as input, the original gait and the noise gait, and outputting an anonymized gait. The proposed method was evaluated using the success rate and mean opinion score (MOS). The success rate is the rate of failed gait recognition, and the MOS is a measure of the naturalness of the anonymized gait. In our experiments, the success rate achieved 98.86% at most while the highest naturalness score is 3.73 in the MOS scale. These findings should open new research directions regarding privacy protection related to gait recognition.","PeriodicalId":305837,"journal":{"name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","volume":"87 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":"117294873","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: 15

Photo forensics from JPEG dimples 图片取证从JPEG酒窝

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267641

S. Agarwal, H. Farid

引用次数: 52

A two factor transformation for speaker verification through ℓ1 comparison 一种通过l1比较验证说话人的双因子变换

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267661

Abelino Jiménez, B. Raj

{"title":"A two factor transformation for speaker verification through ℓ1 comparison","authors":"Abelino Jiménez, B. Raj","doi":"10.1109/WIFS.2017.8267661","DOIUrl":"https://doi.org/10.1109/WIFS.2017.8267661","url":null,"abstract":"In a speaker verification task, speech is used as a unique biometrie identifier of an individual. A speaker presents his credentials along with a voice sample. The system matches the voice sample to its own model for the speaker to accept or reject him. This has many pitfalls. First, speech by itself, is not a sufficiently \"strong\" biometric, and false acceptance is a problem. Second, the user must provide the system with voice samples. This puts the speaker's privacy at risk. The system may infer personal information about the user, such as gender, age, ethnicity, health, etc. Finally, if a malicious entity pilfers the speaker's models from the system, the loss is permanent. The speaker cannot change their voice to re-enroll. In this paper, we present a two-factor transformation that addresses all the above issues. It combines a personal password with speech features in order to increase the performance of a verification system. At the same time it is guaranteed not to not reveal any information about the speech or the password to the system. Finally, it is cancelable; if a model is compromised, the user can re-enroll without risk. In particular, we study a transformation that preserves the ℓ1 distance between features as long as this is smaller than some threshold and the user uses the correct password. Experimental results confirm the theory of the proposal in term of improvement in the system's accuracy, finding conditions to get zero error. Security consequences and feasibility of its implementation are discussed.","PeriodicalId":305837,"journal":{"name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","volume":"23 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":"125838049","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: 4

Security in the shell: An optical physical unclonable function made of shells of cholesteric liquid crystals 壳内安全:由胆甾液晶壳构成的光学物理不可克隆功能

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-10-02 DOI: 10.1109/WIFS.2017.8267644

G. Lenzini, Samir Ouchani, P. B. Rønne, P. Ryan, Y. Geng, Jan P. F. Lagerwall, JungHyun Noh

引用次数: 13

Privacy preserving identification using sparse approximation with ambiguization 使用带有歧义的稀疏逼近的隐私保护识别

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-09-29 DOI: 10.1109/WIFS.2017.8267664

Behrooz Razeghi, S. Voloshynovskiy, Dimche Kostadinov, O. Taran

引用次数: 19

On the feasibility of classification-based product package authentication 基于分类的产品包装认证可行性研究

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-07-02 DOI: 10.1109/WIFS.2017.8267659

R. Schraml, L. Debiasi, Christof Kauba, A. Uhl

引用次数: 11

Image carving with missing headers and missing fragments 缺少头部和碎片的图像雕刻

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-07-02 DOI: 10.1109/WIFS.2017.8267665

Emre Durmus, M. Mohanty, Samet Taspinar, Erkam Uzun, N. Memon

引用次数: 5

Minutia-pair spectral representations for fingerprint template protection 指纹模板保护的细节对谱表示

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-03-20 DOI: 10.1109/WIFS.2017.8267656

Taras Stanko, B. Škorić

引用次数: 3

It's always April fools' day!: On the difficulty of social network misinformation classification via propagation features 永远是愚人节!:基于传播特征的社交网络错误信息分类难度研究

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-01-16 DOI: 10.1109/WIFS.2017.8267653

M. Conti, Daniele Lain, R. Lazzeretti, Giulio Lovisotto, Walter Quattrociocchi

{"title":"It's always April fools' day!: On the difficulty of social network misinformation classification via propagation features","authors":"M. Conti, Daniele Lain, R. Lazzeretti, Giulio Lovisotto, Walter Quattrociocchi","doi":"10.1109/WIFS.2017.8267653","DOIUrl":"https://doi.org/10.1109/WIFS.2017.8267653","url":null,"abstract":"Given the huge impact that Online Social Networks (OSN) had in the way people get informed and form their opinion, they became an attractive playground for malicious entities that want to spread misinformation, and leverage their effect. In fact, misinformation easily spreads on OSN, and this is a huge threat for modern society, possibly influencing also the outcome of elections, or even putting people's life at risk (e.g., spreading \"anti-vaccines\" misinformation). Therefore, it is of paramount importance for our society to have some sort of \"validation\" on information spreading through OSN. The need for a wide-scale validation would greatly benefit from automatic tools. In this paper, we show that it is difficult to carry out an automatic classification of misinformation considering only structural properties of content propagation cascades. We focus on structural properties, because they would be inherently difficult to be manipulated, with the the aim of circumventing classification systems. To support our claim, we carry out an extensive evaluation on Facebook posts belonging to conspiracy theories (representative of misinformation), and scientific news (representative of fact-checked content). Our findings show that conspiracy content reverberates in a way which is hard to distinguish from scientific content: for the classification mechanism we investigated, classification F-score never exceeds 0.7.","PeriodicalId":305837,"journal":{"name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133537724","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: 29

Towards measuring uniqueness of human voice 测量人类声音的独特性

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 1900-01-01 DOI: 10.1109/WIFS.2017.8267666

S. Tandogan, H. Sencar, B. Tavlı

引用次数: 2