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DeepFakes evolution: Analysis of facial regions and fake detection performance

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arXiv 2020年

作者： Tolosana, Ruben Romero-Tapiador, Sergio Fierrez, Julian Vera-Rodriguez, Ruben Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid

Media forensics has attracted a lot of attention in the last years in part due to the increasing concerns around DeepFakes. Since the initial DeepFake databases from the 1stgeneration such as UADFV and FaceForensics++ up to the latest databases of the 2ndgeneration such as Celeb-DF and DFDC, many visual improvements have been carried out, making fake videos almost indistinguishable to the human eye. This study provides an exhaustive analysis of both 1stand 2ndDeep-Fake generations in terms of facial regions and fake detection performance. Two different approaches are considered in our experimental framework: i) selecting the entire face as input to the fake detection system, and ii) selecting specific facial regions such as the eyes or nose, among others, as input to the fake detection system. Among all the findings resulting from our experiments, we highlight the poor fake detection results achieved even by the strongest state-of-the-art fake detectors in the latest DeepFake databases of the 2ndgeneration, with Equal Error Rate results ranging from 15% to 30%. These results remark the necessity of further research in order to develop more sophisticated fake detection systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Fake detection

How Good is ChatGPT at Face biometrics? A First Look into Recognition, Soft biometrics, and Explainability

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arXiv 2024年

作者： DeAndres-Tame, Ivan Tolosana, Ruben Vera-Rodriguez, Ruben Morales, Aythami Fierrez, Julian Ortega-Garcia, Javier Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Spain

Large Language Models (LLMs) such as GPT developed by OpenAI, have already shown astonishing results, introducing quick changes in our society. This has been intensified by the release of ChatGPT which allows anyone to interact in a simple conversational way with LLMs, without any experience in the field needed. As a result, ChatGPT has been rapidly applied to many different tasks such as code- and song-writer, education, virtual assistants, etc., showing impressive results for tasks for which it was not trained (zero-shot learning). The present study aims to explore the ability of ChatGPT, based on the recent GPT-4 multimodal LLM, for the task of face biometrics. In particular, we analyze the ability of ChatGPT to perform tasks such as face verification, soft-biometrics estimation, and explainability of the results. ChatGPT could be very valuable to further increase the explainability and transparency of automatic decisions in human scenarios. Experiments are carried out in order to evaluate the performance and robustness of ChatGPT, using popular public benchmarks and comparing the results with state-of-the-art methods in the field. The results achieved in this study show the potential of LLMs such as ChatGPT for face biometrics, especially to enhance explainability. For reproducibility reasons, we release all the code in GitHub1 © 2024, CC BY-NC-ND.

关键词： Face recognition

SaFL: Sybil-aware Federated Learning with Application to Face Recognition

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SaFL: Sybil-aware Federated Learning with Application to Fac...

Image Processing Challenges (ICIPC), IEEE International Conference on

作者： Mahdi Ghafourian Julian Fierrez Ruben Vera-Rodriguez Ruben Tolosana Aythami Morales Biometrics and Data Pattern Analytics BiDA-Lab Universidad Autonoma de Madrid Spain

ISBN: (数字)9798331515942

ISBN: (纸本)9798331515959

Federated Learning (FL) is a machine learning paradigm to conduct collaborative learning among clients on a joint model. The primary goal is to share clients’ local training parameters with an integrating server while preserving their privacy. This method permits to exploit the potential of massive mobile users’ data for the benefit of machine learning models’ performance while keeping sensitive data on local devices. On the downside, FL raises security and privacy concerns that have just started to be studied. To address some of the key threats in FL, researchers have proposed to use secure aggregation methods (e.g. homomorphic encryption, secure multiparty computation, etc.). These solutions improve some security and privacy metrics, but at the same time bring about other serious threats such as poisoning attacks, backdoor attacks, and free running attacks. This paper proposes a new defense method against poisoning attacks in FL called SaFL (Sybil-aware Federated Learning) that minimizes the effect of sybils with a novel time-variant aggregation scheme.

关键词： Training Performance evaluation Metalearning Privacy Federated learning Prevention and mitigation Computational modeling Security Servers Protection

DeepWriteSYN: On-line handwriting synthesis via deep short-term representations

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arXiv 2020年

作者： Tolosana, Ruben Delgado-Santos, Paula Perez-Uribe, Andres Vera-Rodriguez, Ruben Fierrez, Julian Morales, Aythami Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Switzerland

This study proposes DeepWriteSYN, a novel on-line handwriting synthesis approach via deep short-term representations. It comprises two modules: i) an optional and interchangeable temporal segmentation, which divides the handwriting into short-time segments consisting of individual or multiple concatenated strokes;and ii) the on-line synthesis of those short-time handwriting segments, which is based on a sequence-to-sequence Variational Autoencoder (VAE). The main advantages of the proposed approach are that the synthesis is carried out in short-time segments (that can run from a character fraction to full characters) and that the VAE can be trained on a configurable handwriting dataset. These two properties give a lot of flexibility to our synthesiser, e.g., as shown in our experiments, DeepWriteSYN can generate realistic handwriting variations of a given handwritten structure corresponding to the natural variation within a given population or a given subject. These two cases are developed experimentally for individual digits and handwriting signatures, respectively, achieving in both cases remarkable results. Also, we provide experimental results for the task of on-line signature verification showing the high potential of DeepWriteSYN to improve significantly one-shot learning scenarios. To the best of our knowledge, this is the first synthesis approach capable of generating realistic on-line handwriting in the short term (including handwritten signatures) via deep learning. This can be very useful as a module toward long-term realistic handwriting generation either completely synthetic or as natural variation of given handwriting samples. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation

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arXiv 2022年

作者： Melzi, Pietro Tolosana, Ruben Vera-Rodriguez, Ruben Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Spain

Electrocardiograms (ECGs) have shown unique patterns to distinguish between different subjects and present important advantages compared to other biometric traits, such as difficulty to counterfeit, liveness detection, and ubiquity. Also, with the success of Deep Learning technologies, ECG biometric recognition has received increasing interest in recent years. However, it is not easy to evaluate the improvements of novel ECG proposed methods, mainly due to the lack of public data and standard experimental protocols. In this study, we perform extensive analysis and comparison of different scenarios in ECG biometric recognition. Both verification and identification tasks are investigated, as well as single- and multi-session scenarios. Finally, we also perform single- and multi-lead ECG experiments, considering traditional scenarios using electrodes in the chest and limbs and current user-friendly wearable devices. In addition, we present ECGXtractor, a robust Deep Learning technology trained with an in-house large-scale database and able to operate successfully across various scenarios and multiple databases. We introduce our proposed feature extractor, trained with multiple sinus-rhythm heartbeats belonging to 55,967 subjects, and provide a general public benchmark evaluation with detailed experimental protocol. We evaluate the system performance over four different databases: i) our in-house database, ii) PTB, iii) ECG-ID, and iv) CYBHi. With the widely used PTB database, we achieve Equal Error Rates of 0.14% and 2.06% in verification, and accuracies of 100% and 96.46% in identification, respectively in single- and multi-session analysis. We release the source code, experimental protocol details, and pre-trained models in GitHub to advance in the field. © 2022, CC BY-NC-ND.

关键词： biometrics

DeepFakes and beyond: A survey of face manipulation and fake detection

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arXiv 2020年

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Morales, Aythami Ortega-Garcia, Javier Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Spain

The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake contents with its corresponding implications towards society in this era of fake news. This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. In particular, four types of facial manipulation are reviewed: i) entire face synthesis, ii) face identity swap (DeepFakes), iii) facial attributes manipulation, and iv) facial expression manipulation. For each manipulation type, we provide details regarding manipulation techniques, existing public databases, and key benchmarks for technology evaluation of fake detection methods, including a summary of results from those evaluations. Among the different databases available and discussed in the survey, FaceForensics++ is for example one of the most widely used for detecting both face identity swap and facial expression manipulations, with results in the literature in the range of 90-100% of manipulation detection accuracy. In addition to the survey information, we also discuss trends and provide an outlook of the ongoing work in this field, e.g., the recently announced DeepFake Detection Challenge (DFDC). Copyright © 2020, The Authors. All rights reserved.

关键词： Face recognition

mEBAL: A Multimodal database for Eye Blink Detection and Attention Level Estimation

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arXiv 2020年

作者： Daza, Roberto Morales, Aythami Fierrez, Julian Tolosana, Ruben Biometrics and Data Pattern Analytics BiDA-Lab Universidad Autonoma de Madrid Spain

SaFL: Sybil-aware Federated Learning with Application to Face Recognition

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arXiv 2023年

作者： Ghafourian, Mahdi Fierrez, Julian Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami Biometrics and Data Pattern Analytics BiDA-Lab Universidad Autonoma de Madrid Spain

关键词： Federated learning

DeepSignCX: Signature Complexity Detection using Recurrent Neural Networks

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DeepSignCX: Signature Complexity Detection using Recurrent N...

International Conference on Document Analysis and Recognition

作者： Ruben Vera-Rodriguez Ruben Tolosana Miguel Caruana Gustavo Manzano Carlos Gonzalez-Garcia Julian Fierrez Javier Ortega-Garcia Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Madrid Spain

This paper proposes a novel approach for on-line signature complexity detection based on Recurrent Neural Networks (RNNs). Complexity of handwritten signatures can vary from very simple ones (just a simple flourish) to very complex signatures (including the handwritten full name and complex flourish). Three different complexity levels are proposed: low, medium, and high. Time functions are extracted from the on-line signatures and a system based on RNNs (BLSTM in particular) is trained to classify the three levels of complexity over a ground truth manually labelled database (BiosecurID with 400 subjects). This initial model is used to automatically label a very large database (DeepSignDB) containing over 1500 subjects, which is then used to train the proposed RNN for signature complexity detection. Promising results ca. 85% of accuracy are achieved. This complexity detector could be used as a first stage in a signature verification system in order to train a specific biometric system per signature complexity level and improve the overall system performance.

关键词： Complexity theory databases Biological system modeling Detectors Manuals Handwriting recognition Training