2020 IEEE International Workshop on Information Forensics and Security (WIFS)最新文献_第2页

The Suitability of RSA for Bulk Data Encryption RSA在批量数据加密中的适用性

2020 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2020-12-06 DOI: 10.1109/WIFS49906.2020.9360899

Pranshu Bajpai, Cody Carter, Daria Tarasova, David Ackley, Ian Masterson, Jamie Schmidt, R. Enbody

引用次数: 0

Synchronization Minimizing Statistical Detectability for Side-Informed JPEG Steganography 同步最小化统计可检测的侧面通知JPEG隐写

2020 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2020-12-06 DOI: 10.1109/WIFS49906.2020.9360884

Quentin Giboulot, P. Bas, R. Cogranne

引用次数: 9

ALASKA#2: Challenging Academic Research on Steganalysis with Realistic Images 阿拉斯加#2:具有挑战性的学术研究与现实图像的隐写分析

2020 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2020-12-06 DOI: 10.1109/WIFS49906.2020.9360896

R. Cogranne, Quentin Giboulot, P. Bas

引用次数: 44

Reliable JPEG Forensics via Model Uncertainty 通过模型不确定性可靠的JPEG取证

2020 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2020-12-06 DOI: 10.1109/WIFS49906.2020.9360893

Benedikt Lorch, Anatol Maier, C. Riess

{"title":"Reliable JPEG Forensics via Model Uncertainty","authors":"Benedikt Lorch, Anatol Maier, C. Riess","doi":"10.1109/WIFS49906.2020.9360893","DOIUrl":"https://doi.org/10.1109/WIFS49906.2020.9360893","url":null,"abstract":"Many methods in image forensics are sensitive to varying amounts of JPEG compression. To mitigate this issue, it is either possible to a) build detectors that better generalize to unknown JPEG settings, or to b) train multiple detectors, where each is specialized to a narrow range of JPEG qualities. While the first approach is currently an open challenge, the second approach may silently fail, even for only slight mismatches in training and testing distributions. To alleviate this challenge, we propose a forensic detector that is able to express uncertainty in its predictions. This allows detecting test samples for which the training distribution is not representative. More specifically, we propose Bayesian logistic regression as an instance of an infinite ensemble of classifiers. The ensemble agrees in its predictions from test samples similar to the training data but its predictions diverge for unknown test samples. The applicability of the proposed method is evaluated on the task of detecting JPEG double compression. The detector achieves high performance on two goals simultaneously: It accurately detects double-JPEG compression, and it accurately indicates when the test data is not covered by the training data. We assert that the proposed method can assist a forensic analyst in assessing detector reliability and in anticipating failure cases for specific inputs.","PeriodicalId":354881,"journal":{"name":"2020 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128082677","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: 9

Speech Audio Splicing Detection and Localization Exploiting Reverberation Cues 利用混响线索的语音音频拼接检测和定位

2020 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2020-12-06 DOI: 10.1109/WIFS49906.2020.9360900

Davide Capoferri, Clara Borrelli, Paolo Bestagini, F. Antonacci, A. Sarti, S. Tubaro

{"title":"Speech Audio Splicing Detection and Localization Exploiting Reverberation Cues","authors":"Davide Capoferri, Clara Borrelli, Paolo Bestagini, F. Antonacci, A. Sarti, S. Tubaro","doi":"10.1109/WIFS49906.2020.9360900","DOIUrl":"https://doi.org/10.1109/WIFS49906.2020.9360900","url":null,"abstract":"Manipulating speech audio recordings through splicing is a task within everyone’s reach. Indeed, it is very easy to collect through social media multiple audio recordings from well-known public figures (e.g., actors, politicians, etc.). These can be cut into smaller excerpts that can be concatenated in order to generate new audio content. As a fake speech from a famous person can be used for fake news spreading and negatively impact on the society, the ability of detecting whether a speech recording has been manipulated is a task of great interest in the forensics community. In this work, we focus on speech audio splicing detection and localization. We leverage the idea that distinct recordings may be acquired in different environments, which are typically characterized by distinctive reverberation cues. Exploiting this property, our method estimates inconsistencies in the reverberation time throughout a speech recording. If reverberation inconsistencies are detected, the audio track is tagged as manipulated and the splicing point time instant is estimated.","PeriodicalId":354881,"journal":{"name":"2020 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126552973","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: 10

Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction Landmark Breaker:通过干扰Landmark Extraction来阻碍DeepFake

2020 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2020-12-06 DOI: 10.1109/WIFS49906.2020.9360910

Pu Sun, Yuezun Li, H. Qi, Siwei Lyu

{"title":"Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction","authors":"Pu Sun, Yuezun Li, H. Qi, Siwei Lyu","doi":"10.1109/WIFS49906.2020.9360910","DOIUrl":"https://doi.org/10.1109/WIFS49906.2020.9360910","url":null,"abstract":"The recent development of Deep Neural Networks (DNN) has significantly increased the realism of AI-synthesized faces, with the most notable examples being the DeepFakes. The DeepFake technology can synthesize a face of target subject from a face of another subject, while retains the same face attributes. With the rapidly increased social media portals (Facebook, Instagram, etc), these realistic fake faces rapidly spread though the Internet, causing a broad negative impact to the society. In this paper, we describe Landmark Breaker, the first dedicated method to disrupt facial landmark extraction, and apply it to the obstruction of the generation of DeepFake videos. Our motivation is that disrupting the facial landmark extraction can affect the alignment of input face so as to degrade the DeepFake quality. Our method is achieved using adversarial perturbations. Compared to the detection methods that only work after DeepFake generation, Landmark Breaker goes one step ahead to prevent DeepFake generation. The experiments are conducted on three state-of-the-art facial landmark extractors using the recent Celeb-DF dataset.","PeriodicalId":354881,"journal":{"name":"2020 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121330821","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: 9

The Syndrome-Trellis Sampler for Generative Steganography 用于生成隐写术的综合征-栅格采样器

2020 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2020-12-06 DOI: 10.1109/WIFS49906.2020.9360885

Tamio-Vesa Nakajima, Andrew D. Ker

引用次数: 1

Reinforcement-Based Divide-and-Conquer Strategy for Side-Channel Attacks 基于强化的分而治之的侧信道攻击策略

2020 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2020-12-06 DOI: 10.1109/WIFS49906.2020.9360908

Shan Jin, R. Bettati

引用次数: 0

Threshold audio secret sharing schemes encrypting audio secrets 阈值音频秘密共享方案对音频秘密进行加密

2020 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2020-12-06 DOI: 10.1109/WIFS49906.2020.9360907

Tetsuro Ishizuka, Yodai Watanabe

引用次数: 0

Electromagnetic Fault Injection as a New Forensic Approach for SoCs 电磁故障注入作为一种新的soc取证方法

2020 IEEE International Workshop on Information Forensics and Security (WIFS) Pub Date : 2020-12-06 DOI: 10.1109/WIFS49906.2020.9360902

Clément Gaine, D. Aboulkassimi, S. Pontié, J. Nikolovski, J. Dutertre

引用次数: 16