2023 11th International Workshop on Biometrics and Forensics (IWBF)最新文献_第2页

Semi-synthetic Data Generation for Tattoo Segmentation 纹身分割的半合成数据生成

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157837

Lázaro J. González Soler, C. Rathgeb, Daniel Fischer

引用次数: 0

Fitness-for-Duty Classification using Temporal Sequences of Iris Periocular images 基于虹膜眼周图像时间序列的适应度分类

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157018

Pamela C. Zurita, Daniel P. Benalcazar, Juan E. Tapia

引用次数: 1

On the Use of Cross-module Attention Statistics Pooling for Speaker Verification 跨模块注意统计池在说话人验证中的应用

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157564

J. Alam, A. Fathan

引用次数: 0

Exposing Deepfake Frames through Spectral Analysis of Color Channels in Frequency Domain 利用频域颜色通道的频谱分析揭露深度假帧

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157211

Muhammad Ahmad Amin, Yongjian Hu, Huimin She, Jicheng Li, Yu Guan, Muhammad Zain Amin

{"title":"Exposing Deepfake Frames through Spectral Analysis of Color Channels in Frequency Domain","authors":"Muhammad Ahmad Amin, Yongjian Hu, Huimin She, Jicheng Li, Yu Guan, Muhammad Zain Amin","doi":"10.1109/IWBF57495.2023.10157211","DOIUrl":"https://doi.org/10.1109/IWBF57495.2023.10157211","url":null,"abstract":"Highly realistic deepfakes are generated by employing generative neural networks, even to the point that it is difficult for humans to tell them apart from the real ones. Nowadays they are one of the causes of misrepresentation or misinformation regarding different subjects. The detection of deepfake content is very important. It can be analyzed in different domains, such as spatial domain and frequency domain, or by employing combinations of them. In this work, we first took inspiration from traditional image forensics and performed a comprehensive frequency spectrum analysis on the deepfake frames and their context color channels to detect spectral anomalies and statistical features. We then use the frequency spectrum statistical features to distinguish between pristine and deepfake content using both unsupervised and supervised learning approaches. Finally, we scrutinize the trained deepfake detection models’ generalization capability from the perspective of suggested statistical features across different deepfake datasets and methods. Our analysis demonstrated the effectiveness of statistical features by identifying real and deepfake content with high accuracy, surpassing the performance of several state-of-the-art methods.","PeriodicalId":273412,"journal":{"name":"2023 11th International Workshop on Biometrics and Forensics (IWBF)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116625591","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

Cross-Sensor Micro-Texture Material Classification and Smartphone Acquisition do not go well together 跨传感器微纹理材料分类和智能手机采集不能很好地结合在一起

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157739

Johannes Schuiki, Christof Kauba, H. Hofbauer, A. Uhl

引用次数: 0

Flickr-PAD: New Face High-Resolution Presentation Attack Detection Database Flickr-PAD:新的人脸高分辨率表示攻击检测数据库

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157771

Diego Pasmino, C. Aravena, Juan E. Tapia, C. Busch

引用次数: 0

On the use of Stable Diffusion for creating realistic faces: from generation to detection 关于使用稳定扩散来创建逼真的人脸:从生成到检测

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10156981

Lorenzo Papa, Lorenzo Faiella, Luca Corvitto, Luca Maiano, Irene Amerini

{"title":"On the use of Stable Diffusion for creating realistic faces: from generation to detection","authors":"Lorenzo Papa, Lorenzo Faiella, Luca Corvitto, Luca Maiano, Irene Amerini","doi":"10.1109/IWBF57495.2023.10156981","DOIUrl":"https://doi.org/10.1109/IWBF57495.2023.10156981","url":null,"abstract":"The mass adoption of diffusion models has shown that artificial intelligence (AI) systems can be used to easily generate realistic images. The spread of these technologies paves the way to previously unimaginable creative uses while also raising the possibility of malicious applications. In this work, we propose a critical analysis of the overall pipeline, i.e., from creating realistic human faces with Stable Diffusion v1.5 [1] to recognizing fake ones. We first propose an analysis of the prompts that allow the generation of extremely realistic faces with a human-in-the-loop approach. Our objective is to identify the text prompts that drive the image generation process to obtain realistic photos that resemble everyday portraits captured with any camera. Next, we study how complex it is to recognize these fake contents for both AI-based models and non-expert humans. We conclude that similar to other deepzfake creation techniques, despite some limitations in generalization across different datasets, it is possible to use AI to recognize these contents more accurately than non-expert humans would.","PeriodicalId":273412,"journal":{"name":"2023 11th International Workshop on Biometrics and Forensics (IWBF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126142517","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

MMFV: A Multi-Movement Finger-Video Database for Contactless Fingerprint Recognition MMFV:一种用于非接触式指纹识别的多动作手指视频数据库

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10156919

Aakarsh Malhotra, Mayank Vatsa, Richa Singh

引用次数: 0

Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models 基于去噪扩散概率模型的人脸变形攻击检测

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10156877

Marija Ivanovska, Vitomir Štruc

{"title":"Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models","authors":"Marija Ivanovska, Vitomir Štruc","doi":"10.1109/IWBF57495.2023.10156877","DOIUrl":"https://doi.org/10.1109/IWBF57495.2023.10156877","url":null,"abstract":"Morphed face images have recently become a growing concern for existing face verification systems, as they are relatively easy to generate and can be used to impersonate someone’s identity for various malicious purposes. Efficient Morphing Attack Detection (MAD) that generalizes well across different morphing techniques is, therefore, of paramount importance. Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks. To address this problem, we propose a novel, diffusion–based MAD method in this paper that learns only from the characteristics of bona fide images. Various forms of morphing attacks are then detected by our model as out-of-distribution samples. We perform rigorous experiments over four different datasets (CASIA-WebFace, FRLL-Morphs, FERET-Morphs and FRGC-Morphs) and compare the proposed solution to both discriminatively-trained and once-class MAD models. The experimental results show that our MAD model achieves highly competitive results on all considered datasets.","PeriodicalId":273412,"journal":{"name":"2023 11th International Workshop on Biometrics and Forensics (IWBF)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116733137","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

Unsupervised Face Synthesis Based on Human Traits 基于人类特征的无监督人脸合成

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157232

Roberto Leyva, G. Epiphaniou, C. Maple, Victor Sanchez

引用次数: 0