2021 IEEE International Workshop on Information Forensics and Security (WIFS)最新文献_第2页

Fusing Multiscale Texture and Residual Descriptors for Multilevel 2D Barcode Rebroadcasting Detection 融合多尺度纹理和残差描述子的多层次二维条码重播检测

2021 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2021-12-07 DOI: 10.1109/WIFS53200.2021.9648391

Anselmo Castelo Branco Ferreira, Changsheng Chen, M. Barni

{"title":"Fusing Multiscale Texture and Residual Descriptors for Multilevel 2D Barcode Rebroadcasting Detection","authors":"Anselmo Castelo Branco Ferreira, Changsheng Chen, M. Barni","doi":"10.1109/WIFS53200.2021.9648391","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648391","url":null,"abstract":"Nowadays, 2D barcodes have been widely used for advertisement, mobile payment, and product authentication. However, in applications related to product authentication, an authentic 2D barcode can be illegally copied and attached to a counterfeited product in such a way to bypass the authentication scheme. In this paper, we employ a proprietary 2D barcode pattern and use multimedia forensics methods to analyse the scanning and printing artefacts resulting from the copy (re-broadcasting) attack. A diverse and complementary feature set is proposed to quantify the barcode texture distortions introduced during the illegal copying process. The proposed features are composed of global and local descriptors, which characterize the multi-scale texture appearance and the points of interest distribution, respectively. The proposed descriptors are compared against some existing texture descriptors and deep learning-based approaches under various scenarios, such as cross-datasets and cross-size. Experimental results highlight the practicality of the proposed method in real-world settings.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679531","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: 0

Compare Before You Buy: Privacy-Preserving Selection of Threat Intelligence Providers 购买前比较:威胁情报提供商的隐私保护选择

2021 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2021-12-07 DOI: 10.1109/WIFS53200.2021.9648381

Jelle Vos, Z. Erkin, C. Doerr

{"title":"Compare Before You Buy: Privacy-Preserving Selection of Threat Intelligence Providers","authors":"Jelle Vos, Z. Erkin, C. Doerr","doi":"10.1109/WIFS53200.2021.9648381","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648381","url":null,"abstract":"In their pursuit to maximize their return on investment, cybercriminals will likely reuse as much as possible between their campaigns. Not only will the same phishing mail be sent to tens of thousands of targets, but reuse of the tools and infrastructure across attempts will lower their costs of doing business. This reuse, however, creates an effective angle for mitigation, as defenders can recognize domain names, attachments, tools, or systems used in a previous compromisation attempt, significantly increasing the cost to the adversary as it would become necessary to recreate the attack infrastructure each time. However, generating such cyber threat intelligence (CTI) is resource-intensive, so organizations often turn to CTI providers that commercially sell feeds with such indicators. As providers have different sources and methods to obtain their data, the coverage and relevance of feeds will vary for each of them. To cover the multitude of threats one organization faces, they are best served by obtaining feeds from multiple providers. However, these feeds may overlap, causing an organization to pay for indicators they already obtained through another provider. This paper presents a privacy-preserving protocol that allows an organization to query the databases of multiple data providers to obtain an estimate of their total coverage without revealing the data they store. In this way, a customer can make a more informed decision on their choice of CTI providers. We implement this protocol in Rust to validate its performance experimentally: When performed between three CTI providers who collectively have 20,000 unique indicators, our protocol takes less than 6 seconds to execute. The code for our experiments is freely available.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132456614","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: 1

Apart from In-field Sensor Defects, are there Additional Age Traces Hidden in a Digital Image? 除了现场传感器缺陷外，数字图像中是否隐藏着额外的年龄痕迹?

2021 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2021-12-07 DOI: 10.1109/WIFS53200.2021.9648396

Robert Jöchl, A. Uhl

引用次数: 2

Spoofing Speaker Verification With Voice Style Transfer And Reconstruction Loss 语音风格转移和重建损失的欺骗说话人验证

2021 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2021-12-07 DOI: 10.1109/WIFS53200.2021.9648375

Thomas Thebaud, Gaël Le Lan, A. Larcher

引用次数: 2

How are PDF files published in the Scientific Community? PDF文件是如何在科学界发表的?

2021 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2021-12-07 DOI: 10.1109/WIFS53200.2021.9648374

Supriya Adhatarao, C. Lauradoux

引用次数: 0

On the Recognition Performance of BioHashing on state-of-the-art Face Recognition models 生物哈希在最先进的人脸识别模型上的识别性能研究

2021 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2021-12-07 DOI: 10.1109/WIFS53200.2021.9648382

Hatef Otroshi-Shahreza, Vedrana Krivokuća Hahn, S. Marcel

{"title":"On the Recognition Performance of BioHashing on state-of-the-art Face Recognition models","authors":"Hatef Otroshi-Shahreza, Vedrana Krivokuća Hahn, S. Marcel","doi":"10.1109/WIFS53200.2021.9648382","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648382","url":null,"abstract":"Face recognition has become a popular authentication tool in recent years. Modern state-of-the-art (SOTA) face recognition methods rely on deep neural networks, which extract discriminative features from face images. Although these methods have high recognition performance, the extracted features contain privacy-sensitive information. Hence, the users' privacy would be jeopardized if the features stored in the face recognition system were compromised. Accordingly, protecting the extracted face features (templates) is an essential task in face recognition systems. In this paper, we use BioHashing for face template protection and aim to establish the minimum BioHash length that would be required in order to maintain the recognition accuracy achieved by the corresponding unprotected system. We consider two hypotheses and experimentally show that the performance depends on the value of the BioHash length (as opposed to the ratio of the BioHash length to the dimension of the original features). To eliminate bias in our experiments, we use several SOTA face recognition models with different network structures, loss functions, and training datasets, and we evaluate these models on two different datasets (LFW and MOBIO). We provide an open-source implementation of all the experiments presented in this paper so that other researchers can verify our findings and build upon our work.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"1896 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130059494","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: 6

Time Scaling Detection and Estimation in Audio Recordings 音频记录中的时间尺度检测与估计

2021 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2021-12-07 DOI: 10.1109/WIFS53200.2021.9648389

M. Pilia, S. Mandelli, Paolo Bestagini, S. Tubaro

引用次数: 3

Impact of Super-Resolution and Human Identification in Drone Surveillance 超分辨率和人的识别在无人机监视中的影响

2021 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2021-12-07 DOI: 10.1109/WIFS53200.2021.9648399

Akshay Agarwal, N. Ratha, Mayank Vatsa, Richa Singh

{"title":"Impact of Super-Resolution and Human Identification in Drone Surveillance","authors":"Akshay Agarwal, N. Ratha, Mayank Vatsa, Richa Singh","doi":"10.1109/WIFS53200.2021.9648399","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648399","url":null,"abstract":"In the scene of large crowd gatherings and challenging visiting places such as rough hills and high glass buildings, acquisition of the images through normal cameras is difficult and next to impossible. In all such scenarios, the drone becomes a useful acquisition sensor to capture the detailed information of the scene and the objects present there. With the rapid development of consumer unmanned aerial vehicles (UAV) or drones, the utilization of these devices became extremely easy. The popular use-case of the drones can be seen for the surveillance to identify any possible threats in the large crowd gathering and recognize the different individuals present in the crowd. However, the images captured using the drones are generally taken from a significant distance to avoid any collision; hence these images generally suffer in quality such as low resolution, motion blur, and other environmental factors. The impact of these artifacts has been seen in the face recognition performance using several machine learning algorithms on large-scale drone databases namely Drone SURF. In this research, we intend to tackle the above artifacts by looking at the problem from the perspective of super-resolution of low-quality images. We have studied state-of-the-art (SOTA) super-resolution algorithms and see whether current methods are capable of handling the challenges of drone images. Apart from that, we have also evaluated another SOTA deep network developed for object detection for human segmentation in drone images. The proposed research provides interesting findings highlighting the limitations of existing works from the perspective of handling drone images. We would like the readers to go through the paper to find out the current limitations and possible future directions in drone image surveillance.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128348280","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: 2

3D Print-Scan Resilient Localized Mesh Watermarking 3D打印扫描弹性局部网格水印

2021 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2021-12-07 DOI: 10.1109/WIFS53200.2021.9648379

Yanmei Chen, Zehua Ma, Hang Zhou, Weiming Zhang

引用次数: 1

Data Augmentation for JPEG Steganalysis 用于JPEG隐写分析的数据增强

2021 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2021-12-07 DOI: 10.1109/WIFS53200.2021.9648390

T. Itzhaki, Yassine Yousfi, J. Fridrich

引用次数: 2