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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

DeepSign: Deep on-line signature verification

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arXiv 2020年

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

Deep learning has become a breathtaking technology in the last years, overcoming traditional handcrafted approaches and even humans for many different tasks. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel proposed approaches as different databases and experimental protocols are usually considered. The main contributions of this study are: i) we provide an in-depth analysis of state-of-the-art deep learning approaches for on-line signature verification, ii) we present and describe the new DeepSignDB on-line handwritten signature biometric public database1, iii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art, and iv) we adapt and evaluate our recent deep learning approach named Time-Aligned Recurrent Neural Networks (TA-RNNs)2 for the task of on-line handwritten signature verification. This approach combines the potential of Dynamic Time Warping and Recurrent Neural Networks to train more robust systems against forgeries. Our proposed TA-RNN system outperforms the state of the art, achieving results even below 2.0% EER when considering skilled forgery impostors and just one training signature per user. Copyright © 2020, The Authors. All rights reserved.

关键词： biometrics

Do You Need More data? The DeepSignDB On-Line Handwritten Signature Biometric database

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Do You Need More Data? The DeepSignDB On-Line Handwritten Si...

International Conference on Document Analysis and Recognition

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

data have become one of the most valuable things in this new era where deep learning technology seems to overcome traditional approaches. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel approaches compared with the state of the art as different experimental protocols and conditions are usually considered for different signature databases. To tackle all these mentioned problems, the main contribution of this study is twofold: i) we present and describe the new DeepSignDB on-line handwritten signature biometric public database, and ii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art. The DeepSignDB database is obtained through the combination of some of the most popular on-line signature databases, and a novel dataset not presented yet. It comprises more than 70K signatures acquired using both stylus and finger inputs from a total of 1526 users. Two acquisition scenarios are considered, office and mobile, with a total of 8 different devices. Additionally, different types of impostors and number of acquisition sessions are considered along the database. The DeepSignDB and benchmark results are available in GitHub.

关键词： databases Forgery Writing Training Protocols Task analysis Deep learning

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

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

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

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

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. © 2023, CC BY-NC-ND.

关键词： Federated learning

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

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

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