2017 IEEE Workshop on Information Forensics and Security (WIFS)最新文献

Retrieving dates in smart card dumps is as hard as finding a needle in a haystack 在智能卡转储中检索日期就像大海捞针一样困难

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-04 DOI: 10.1109/WIFS.2017.8267663

Thomas Gougeon, Morgan Barbier, Patrick Lacharme, Gildas Avoine, C. Rosenberger

引用次数: 1

The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality 学习对对抗性扰动的脆弱性随着内在维度的增加而增加

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-04 DOI: 10.1109/WIFS.2017.8267651

L. Amsaleg, J. Bailey, Dominique Barbe, S. Erfani, M. Houle, Vinh Nguyen, Miloš Radovanović

{"title":"The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality","authors":"L. Amsaleg, J. Bailey, Dominique Barbe, S. Erfani, M. Houle, Vinh Nguyen, Miloš Radovanović","doi":"10.1109/WIFS.2017.8267651","DOIUrl":"https://doi.org/10.1109/WIFS.2017.8267651","url":null,"abstract":"Recent research has shown that machine learning systems, including state-of-the-art deep neural networks, are vulnerable to adversarial attacks. By adding to the input object an imperceptible amount of adversarial noise, it is highly likely that the classifier can be tricked into assigning the modified object to any desired class. It has also been observed that these adversarial samples generalize well across models. A complete understanding of the nature of adversarial samples has not yet emerged. Towards this goal, we present a novel theoretical result formally linking the adversarial vulnerability of learning to the intrinsic dimensionality of the data. In particular, our investigation establishes that as the local intrinsic dimensionality (LID) increases, 1-NN classifiers become increasingly prone to being subverted. We show that in expectation, a k-nearest neighbor of a test point can be transformed into its 1-nearest neighbor by adding an amount of noise that diminishes as the LID increases. We also provide an experimental validation of the impact of LID on adversarial perturbation for both synthetic and real data, and discuss the implications of our result for general classifiers.","PeriodicalId":305837,"journal":{"name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128511161","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: 57

Secure identification under jamming attacks 在干扰攻击下安全识别

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267648

H. Boche, C. Deppe

引用次数: 5

Audio phylogenetic analysis using geometric transforms 使用几何变换的音频系统发育分析

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267650

Sebastiano Verde, S. Milani, Paolo Bestagini, S. Tubaro

引用次数: 3

Linking received packet to the transmitter through physical-fingerprinting of controller area network 通过控制器局域网的物理指纹将接收到的数据包链接到发送器

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267643

Omid Avatefipour, Azeem Hafeez, M. Tayyab, Hafiz Malik

{"title":"Linking received packet to the transmitter through physical-fingerprinting of controller area network","authors":"Omid Avatefipour, Azeem Hafeez, M. Tayyab, Hafiz Malik","doi":"10.1109/WIFS.2017.8267643","DOIUrl":"https://doi.org/10.1109/WIFS.2017.8267643","url":null,"abstract":"The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time-and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.","PeriodicalId":305837,"journal":{"name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","volume":"41 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":"128747009","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: 39

Enhanced free-text keystroke continuous authentication based on dynamics of wrist motion 增强自由文本击键连续认证基于手腕运动的动态

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267642

Borui Li, Han Sun, Yang Gao, V. Phoha, Zhanpeng Jin

{"title":"Enhanced free-text keystroke continuous authentication based on dynamics of wrist motion","authors":"Borui Li, Han Sun, Yang Gao, V. Phoha, Zhanpeng Jin","doi":"10.1109/WIFS.2017.8267642","DOIUrl":"https://doi.org/10.1109/WIFS.2017.8267642","url":null,"abstract":"Free-text keystroke is a form of behavioral biometrics which has great potential for addressing the security limitations of conventional one-time authentication by continuously monitoring the user's typing behaviors. This paper presents a new, enhanced continuous authentication approach by incorporating the dynamics of both keystrokes and wrist motions. Based upon two sets of features (free-text keystroke latency features and statistical wrist motion patterns extracted from the wrist-worn smartwatches), two one-vs-all Random Forest Ensemble Classifiers (RFECs) are constructed and trained respectively. A Dynamic Trust Model (DTM) is then developed to fuse the two classifiers' decisions and realize non-time-blocked real-time authentication. In the free-text typing experiments involving 25 human subjects, an imposter/intruder can be detected within no more than one sentence (average 56 keystrokes) with an FRR of 1.82% and an FAR of 1.94%. Compared with the scheme relying on only keystroke latency which has an FRR of 4.66%, an FAR of 17.92% and the required number of keystroke of 162, the proposed authentication system shows significant improvements in terms of accuracy, efficiency, and usability.","PeriodicalId":305837,"journal":{"name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","volume":"29 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":"124114705","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: 11

AHEad: Privacy-preserving online behavioural advertising using homomorphic encryption 展望:使用同态加密保护隐私的在线行为广告

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267662

L. Helsloot, Gamze Tillem, Z. Erkin

引用次数: 9

Energy obfuscation for compressive encryption and processing 压缩加密和处理的能量混淆

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267649

Matteo Testa, T. Bianchi, E. Magli

引用次数: 5

Object insertion and removal in images with mirror reflection 在具有镜像反射的图像中插入和删除对象

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267645

Zhaohui H. Sun, A. Hoogs

引用次数: 4

A gradient-based pixel-domain attack against SVM detection of global image manipulations 基于梯度的像素域攻击对SVM检测的全局图像处理

2017 IEEE Workshop on Information Forensics and Security (WIFS) Pub Date : 2017-12-01 DOI: 10.1109/WIFS.2017.8267668

Z. Chen, B. Tondi, Xiaolong Li, R. Ni, Yao Zhao, M. Barni

{"title":"A gradient-based pixel-domain attack against SVM detection of global image manipulations","authors":"Z. Chen, B. Tondi, Xiaolong Li, R. Ni, Yao Zhao, M. Barni","doi":"10.1109/WIFS.2017.8267668","DOIUrl":"https://doi.org/10.1109/WIFS.2017.8267668","url":null,"abstract":"We present a gradient-based attack against SVM-based forensic techniques relying on high-dimensional SPAM features. As opposed to prior work, the attack works directly in the pixel domain even if the relationship between pixel values and SPAM features can not be inverted. The proposed method relies on the estimation of the gradient of the SVM output with respect to pixel values, however it departs from gradient descent methodology due to the necessity of preserving the integer nature of pixels and to reduce the effect of the attack on image quality. A fast algorithm to estimate the gradient is also introduced to reduce the complexity of the attack. We tested the proposed attack against SVM detection of histogram stretching, adaptive histogram equalization and median filtering. In all cases the attack succeeded in inducing a decision error with a very limited distortion, the PSNR between the original and the attacked images ranging from 50 to 70 dBs. The attack is also effective in the case of attacks with Limited Knowledge (LK) when the SVM used by the attacker is trained on a different dataset with respect to that used by the analyst.","PeriodicalId":305837,"journal":{"name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","volume":"98 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":"122641070","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