Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security最新文献_第2页

How Private is Machine Learning? 机器学习有多私密?

Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security Pub Date : 2021-06-17 DOI: 10.1145/3437880.3458440

Nicolas Carlini

引用次数: 1

DNN Watermarking: Four Challenges and a Funeral DNN水印:四个挑战和葬礼

Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security Pub Date : 2021-06-17 DOI: 10.1145/3437880.3460399

M. Barni, F. Pérez-González, B. Tondi

引用次数: 16

General Requirements on Synthetic Fingerprint Images for Biometric Authentication and Forensic Investigations 生物识别鉴定和法医调查用合成指纹图像的一般要求

Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security Pub Date : 2021-06-17 DOI: 10.1145/3437880.3460410

A. Makrushin, Christof Kauba, Simon Kirchgasser, Stefan Seidlitz, Christian Kraetzer, A. Uhl, J. Dittmann

{"title":"General Requirements on Synthetic Fingerprint Images for Biometric Authentication and Forensic Investigations","authors":"A. Makrushin, Christof Kauba, Simon Kirchgasser, Stefan Seidlitz, Christian Kraetzer, A. Uhl, J. Dittmann","doi":"10.1145/3437880.3460410","DOIUrl":"https://doi.org/10.1145/3437880.3460410","url":null,"abstract":"Generation of synthetic biometric samples such as, for instance, fingerprint images gains more and more importance especially in view of recent cross-border regulations on security of private data. The reason is that biometric data is designated in recent regulations such as the EU GDPR as a special category of private data, making sharing datasets of biometric samples hardly possible even for research purposes. The usage of fingerprint images in forensic research faces the same challenge. The replacement of real datasets by synthetic datasets is the most advantageous straightforward solution which bears, however, the risk of generating \"unrealistic\" samples or \"unrealistic distributions\" of samples which may visually appear realistic. Despite numerous efforts to generate high-quality fingerprints, there is still no common agreement on how to define \"high-quality'' and how to validate that generated samples are realistic enough. Here, we propose general requirements on synthetic biometric samples (that are also applicable for fingerprint images used in forensic application scenarios) together with formal metrics to validate whether the requirements are fulfilled. Validation of our proposed requirements enables establishing the quality of a generative model (informed evaluation) or even the quality of a dataset of generated samples (blind evaluation). Moreover, we demonstrate in an example how our proposed evaluation concept can be applied to a comparison of real and synthetic datasets aiming at revealing if the synthetic samples exhibit significantly different properties as compared to real ones.","PeriodicalId":120300,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114647318","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

Improving EfficientNet for JPEG Steganalysis 对JPEG隐写分析的改进

Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security Pub Date : 2021-06-17 DOI: 10.1145/3437880.3460397

Yassine Yousfi, Jan Butora, J. Fridrich, Clément Fuji Tsang

引用次数: 22

Deep Neural Exposure: You Can Run, But Not Hide Your Neural Network Architecture! 深度神经暴露:你可以运行，但不能隐藏你的神经网络架构!

Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security Pub Date : 2021-06-17 DOI: 10.1145/3437880.3460415

Sayed Erfan Arefin, Abdul Serwadda

引用次数: 1

White-Box Watermarking Scheme for Fully-Connected Layers in Fine-Tuning Model 微调模型中全连通层的白盒水印方案

Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security Pub Date : 2021-06-17 DOI: 10.1145/3437880.3460402

M. Kuribayashi, Takuro Tanaka, Shunta Suzuki, Tatsuya Yasui, N. Funabiki

引用次数: 14

PRNU-based Deepfake Detection 基于prnu的Deepfake检测

Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security Pub Date : 2021-06-17 DOI: 10.1145/3437880.3460400

Florian Lugstein, S. Baier, Gregor Bachinger, A. Uhl

引用次数: 14

Meta and Media Data Stream Forensics in the Encrypted Domain of Video Conferences 视频会议加密域的元数据流和媒体数据流取证

Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security Pub Date : 2021-06-17 DOI: 10.1145/3437880.3460412

R. Altschaffel, Jonas Hielscher, Stefan Kiltz, J. Dittmann

{"title":"Meta and Media Data Stream Forensics in the Encrypted Domain of Video Conferences","authors":"R. Altschaffel, Jonas Hielscher, Stefan Kiltz, J. Dittmann","doi":"10.1145/3437880.3460412","DOIUrl":"https://doi.org/10.1145/3437880.3460412","url":null,"abstract":"Our paper presents a systematic approach to investigate whether and how events can be identified and extracted during the use of video conferencing software. Our approach is based on the encrypted meta and multimedia data exchanged during video conference sessions. It relies on the network data stream which contains data interpretable without decryption (plain data) and encrypted data (encrypted content) some of which is decrypted using our approach (decrypted content). This systematic approach uses a forensic process model and the fission of network data streams before applying methods on the specific individual data types. Our approach is applied exemplary to the Zoom Videoconferencing Service with Client Version 5.4.57862.0110 [4], the mobile Android App Client Version 5.5.2 (1328) [4], the webbased client and the servers (accessed between Jan 21st and Feb 4th). The investigation includes over 50 different configurations. For the heuristic speaker identification, two series of nine sets for eight different speakers are collected. The results show that various user data can be derived from characteristics of encrypted media streams, even if end-to-end encryption is used. The findings suggest user privacy risks. Our approach offers the identification of various events, which enable activity tracking (e.g. camera on/off, increased activity in front of camera) by evaluating heuristic features of the network streams. Further research into user identification within the encrypted audio stream based on pattern recognition using heuristic features of the corresponding network data stream is conducted and suggests the possibility to identify users within a specific set.","PeriodicalId":120300,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122037346","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: 3

Fake Speech Detection Using Residual Network with Transformer Encoder 基于变压器编码器的残差网络假语音检测

Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security Pub Date : 2021-06-17 DOI: 10.1145/3437880.3460408

Zhenyu Zhang, Xiaowei Yi, Xianfeng Zhao

{"title":"Fake Speech Detection Using Residual Network with Transformer Encoder","authors":"Zhenyu Zhang, Xiaowei Yi, Xianfeng Zhao","doi":"10.1145/3437880.3460408","DOIUrl":"https://doi.org/10.1145/3437880.3460408","url":null,"abstract":"Fake speech detection aims to distinguish fake speech from natural speech. This paper presents an effective fake speech detection scheme based on residual network with transformer encoder (TE-ResNet) for improving the performance of fake speech detection. Firstly, considering inter-frame correlation of the speech signal, we utilize transformer encoder to extract contextual representations of the acoustic features. Then, a residual network is used to process deep features and calculate score that the speech is fake. Besides, to increase the quantity of training data, we apply five speech data augmentation techniques on the training dataset. Finally, we fuse the different fake speech detection models on score-level by logistic regression for compensating the shortcomings of each single model. The proposed scheme is evaluated on two public speech datasets. Our experiments demonstrate that the proposed TE-ResNet outperforms the existing state-of-the-art methods both on development and evaluation datasets. In addition, the proposed fused model achieves improved performance for detection of unseen fake speech technology, which can obtain equal error rates (EERs) of 3.99% and 5.89% on evaluation set of FoR-normal dataset and ASVspoof 2019 LA dataset respectively.","PeriodicalId":120300,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132066016","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

How to Pretrain for Steganalysis 如何对隐写分析进行预训练

Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security Pub Date : 2021-06-17 DOI: 10.1145/3437880.3460395

Jan Butora, Yassine Yousfi, J. Fridrich

引用次数: 25