ICPR Workshops最新文献_第2页

Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding 基于多视图知识图嵌入的文物地理定位

ICPR Workshops Pub Date : 2022-09-08 DOI: 10.48550/arXiv.2209.03638

Heba Mohamed, Sebastiano Vascon, Feliks Hibraj, Stuart James, Diego Pilutti, A. D. Bue, M. Pelillo

引用次数: 0

Statistical Methods for Assessing Differences in False Non-match Rates Across Demographic Groups 评估不同人口统计学组间假不匹配率差异的统计方法

ICPR Workshops Pub Date : 2022-08-21 DOI: 10.1007/978-3-031-37731-0_41

M. Schuckers, Sandip Purnapatra, K. Fatima, Daqing Hou, S. Schuckers

引用次数: 0

Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups 深度生成视图减轻性别-种族群体的性别分类偏见

ICPR Workshops Pub Date : 2022-08-17 DOI: 10.48550/arXiv.2208.08382

Sreeraj Ramachandran, A. Rattani

引用次数: 5

Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels 无明确噪声标签的噪声心电图信号的自动检测

ICPR Workshops Pub Date : 2022-08-08 DOI: 10.48550/arXiv.2208.08853

Radhika Dua, Jiyoung Lee, J. Kwon, E. Choi

引用次数: 0

SERCNN: Stacked Embedding Recurrent Convolutional Neural Network in Detecting Depression on Twitter SERCNN:堆叠嵌入循环卷积神经网络在Twitter上检测抑郁

ICPR Workshops Pub Date : 2022-07-29 DOI: 10.1007/978-3-031-37660-3_43

Heng Ee Tay, M. Lim, Chun Yong Chong

引用次数: 0

GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection GBDF:性别平衡的DeepFake数据集，用于公平的DeepFake检测

ICPR Workshops Pub Date : 2022-07-21 DOI: 10.48550/arXiv.2207.10246

Aakash Varma Nadimpalli, A. Rattani

{"title":"GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection","authors":"Aakash Varma Nadimpalli, A. Rattani","doi":"10.48550/arXiv.2207.10246","DOIUrl":"https://doi.org/10.48550/arXiv.2207.10246","url":null,"abstract":"Facial forgery by deepfakes has raised severe societal concerns. Several solutions have been proposed by the vision community to effectively combat the misinformation on the internet via automated deepfake detection systems. Recent studies have demonstrated that facial analysis-based deep learning models can discriminate based on protected attributes. For the commercial adoption and massive roll-out of the deepfake detection technology, it is vital to evaluate and understand the fairness (the absence of any prejudice or favoritism) of deepfake detectors across demographic variations such as gender and race. As the performance differential of deepfake detectors between demographic subgroups would impact millions of people of the deprived sub-group. This paper aims to evaluate the fairness of the deepfake detectors across males and females. However, existing deepfake datasets are not annotated with demographic labels to facilitate fairness analysis. To this aim, we manually annotated existing popular deepfake datasets with gender labels and evaluated the performance differential of current deepfake detectors across gender. Our analysis on the gender-labeled version of the datasets suggests (a) current deepfake datasets have skewed distribution across gender, and (b) commonly adopted deepfake detectors obtain unequal performance across gender with mostly males outperforming females. Finally, we contributed a gender-balanced and annotated deepfake dataset, GBDF, to mitigate the performance differential and to promote research and development towards fairness-aware deep fake detectors. The GBDF dataset is publicly available at: https://github.com/aakash4305/GBDF","PeriodicalId":391161,"journal":{"name":"ICPR Workshops","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132190369","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: 13

Comparing Feature Importance and Rule Extraction for Interpretability on Text Data 文本数据可解释性特征重要性与规则提取的比较

ICPR Workshops Pub Date : 2022-07-04 DOI: 10.48550/arXiv.2207.01420

Gianluigi Lopardo, D. Garreau

引用次数: 1

Comparison of attention models and post-hoc explanation methods for embryo stage identification: a case study 胚胎阶段识别的注意模型与事后解释方法之比较:个案研究

ICPR Workshops Pub Date : 2022-05-13 DOI: 10.48550/arXiv.2205.06546

T. Gomez, Thomas Fr'eour, H. Mouchère

引用次数: 2

Evaluating Proposed Fairness Models for Face Recognition Algorithms 评价人脸识别算法的公平性模型

ICPR Workshops Pub Date : 2022-03-09 DOI: 10.48550/arXiv.2203.05051

John J. Howard, Eli J. Laird, Yevgeniy B. Sirotin, Rebecca E. Rubin, Jerry L. Tipton, A. Vemury

{"title":"Evaluating Proposed Fairness Models for Face Recognition Algorithms","authors":"John J. Howard, Eli J. Laird, Yevgeniy B. Sirotin, Rebecca E. Rubin, Jerry L. Tipton, A. Vemury","doi":"10.48550/arXiv.2203.05051","DOIUrl":"https://doi.org/10.48550/arXiv.2203.05051","url":null,"abstract":"The development of face recognition algorithms by academic and commercial organizations is growing rapidly due to the onset of deep learning and the widespread availability of training data. Though tests of face recognition algorithm performance indicate yearly performance gains, error rates for many of these systems differ based on the demographic composition of the test set. These\"demographic differentials\"in algorithm performance can contribute to unequal or unfair outcomes for certain groups of people, raising concerns with increased worldwide adoption of face recognition systems. Consequently, regulatory bodies in both the United States and Europe have proposed new rules requiring audits of biometric systems for\"discriminatory impacts\"(European Union Artificial Intelligence Act) and\"fairness\"(U.S. Federal Trade Commission). However, no standard for measuring fairness in biometric systems yet exists. This paper characterizes two proposed measures of face recognition algorithm fairness (fairness measures) from scientists in the U.S. and Europe. We find that both proposed methods are challenging to interpret when applied to disaggregated face recognition error rates as they are commonly experienced in practice. To address this, we propose a set of interpretability criteria, termed the Functional Fairness Measure Criteria (FFMC), that outlines a set of properties desirable in a face recognition algorithm fairness measure. We further develop a new fairness measure, the Gini Aggregation Rate for Biometric Equitability (GARBE), and show how, in conjunction with the Pareto optimization, this measure can be used to select among alternative algorithms based on the accuracy/fairness trade-space. Finally, we have open-sourced our dataset of machine-readable, demographically disaggregated error rates. We believe this is currently the largest open-source dataset of its kind.","PeriodicalId":391161,"journal":{"name":"ICPR Workshops","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127822130","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

The Effect of Model Compression on Fairness in Facial Expression Recognition 模型压缩对面部表情识别公平性的影响

ICPR Workshops Pub Date : 2022-01-05 DOI: 10.1007/978-3-031-37745-7_9

Samuil Stoychev, H. Gunes

引用次数: 10