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Improving Face presentation attack detection Through Deformable Convolution and Transfer Learning

IEEE ACCESS 2025年 13卷 31228-31238页

作者： Muhammad Ibrahim, Shakeel Sohail Ibrahim, Muhammad Khan, Shujaat Ko, Young-Woong Lee, Jeong-Gun Hallym Univ Dept Comp Engn Chuncheon Si 24252 South Korea GEOMEXSOFT Co Ltd Chuncheon Si 24461 South Korea Kumoh Natl Inst Technol Dept Mech Syst Engn Gumi Si 39177 South Korea King Fahd Univ Petr & Minerals SDAIA KFUPM Joint Res Ctr Artificial Intelligence Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Coll Comp & Math Dept Comp Engn Dhahran 31261 Saudi Arabia

Face presentation attack detection (PAD) is essential for ensuring the security and reliability of face recognition systems by preventing unauthorized access through spoofing attempts. attackers can exploit various methods, such as printed photos, video replays, paper masks, 3D masks, or makeup, to imitate a legitimate user's biometric traits. In this paper, we propose an enhanced face PAD solution that leverages the deformable convolutional layer within the MobileNetV2 architecture to improve detection accuracy. By replacing the standard convolution layer with a Deformable ConvNets V2, the proposed model adapts dynamically to spatial distortions, capturing more detailed and robust features for effective face PAD. Extensive experiments on the Replay-attack, Replay-Mobile, ROSE-Youtu, OULU-NPU, and SiW-Mv2 datasets validate the superiority of the proposed approach. The method achieves a half total error rate (HTER) of 0.0% on both the Replay-attack and Replay-Mobile datasets, 1.26% on ROSE-Youtu, 4.88% on SiW-Mv2, and an ACER of 0.208% on OULU-NPU, outperforming several existing methods. These results highlight the robustness and effectiveness of our approach in safeguarding face recognition systems against presentation attacks.

关键词： Deep learning anti-spoofing face liveness detection deformable convolution presentation attack detection Deep learning anti-spoofing face liveness detection deformable convolution presentation attack detection

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MoSFPAD: An end-to-end ensemble of MobileNet and Support Vector Classifier for fingerprint presentation attack detection

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COMPUTERS & SECURITY 2025年 148卷

作者： Rai, Anuj Dey, Somnath Patidar, Pradeep Rai, Prakhar Indian Inst Technol Indore Dept Comp Sci & Engn Indore 453552 Madhya Pradesh India

Automatic fingerprint recognition systems are the most extensively used systems for person authentication although they are vulnerable to presentation attacks. Artificial artifacts created with the help of various materials are used to deceive these systems causing a threat to the security of fingerprint-based applications. This paper proposes a novel end-to-end model to detect fingerprint presentation attacks. The proposed model incorporates MobileNet as a feature extractor and a Support Vector Classifier as a classifier to detect presentation attacks in cross-material and cross-sensor paradigms. The feature extractor's parameters are learned with the loss generated by the support vector classifier. The proposed model eliminates the need for intermediary data preparation procedures, unlike other static hybrid architectures. The performance of the proposed model has been validated on benchmark LivDet 2011, 2013, 2015, 2017, and 2019 databases, and overall accuracy of 98.64%, 99.50%, 97.23%, 95.06%, and 95.20% are achieved on these databases, respectively. The performance of the proposed model is compared with state-of-the-art methods and is able to reduce the average classification error of 3.63%, 1.86%, 1.83%, 0.05%, 0.93% on LivDet 2011, 2013, 2015, 2017, and 2019 databases, respectively for same and cross material protocols in intra-sensor paradigm. The proposed method also reduced the average classification error of 1.59%, 1.41%, and 2.29% for LivDet 2011, 2013, and 2017 databases, respectively for the cross-sensor paradigm. It is evident from the results that the proposed method outperforms state-of-the-art methods in intra-sensor as well as cross-sensor paradigms in terms of average classification error.

关键词： Fingerprint Biometrics presentation attack detection Hybrid Architecture Support Vector Classifier Deep-learning

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presentation attack detection for Iris Recognition: An Assessment of the State-of-the-Art

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ACM COMPUTING SURVEYS 2018年第4期51卷 1–35页

作者： Czajka, Adam Bowyer, Kevin W. Res & Acad Comp Network NASK Kolska 12 PL-01045 Warsaw Poland Univ Notre Dame 384 Fitzpatrick Hall Notre Dame IN 46556 USA

Iris recognition is increasingly used in large-scale applications. As a result, presentation attack detection for iris recognition takes on fundamental importance. This survey covers the diverse research literature on this topic. Different categories of presentation attack are described and placed in an application-relevant framework, and the state of the art in detecting each category of attack is summarized. One conclusion from this is that presentation attack detection for iris recognition is not yet a solved problem. Datasets available for research are described, research directions for the near- and medium-term future are outlined, and a short list of recommended readings is suggested.

关键词： presentation attack detection liveness detection anti-spoofing iris recognition

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presentation attack detection for Face Recognition Using Light Field Camera

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2015年第3期24卷 1060-1075页

作者： Raghavendra, R. Raja, Kiran B. Busch, Christoph Gjovik Univ Coll Norwegian Biometr Lab N-2815 Gjovik Norway

The vulnerability of face recognition systems is a growing concern that has drawn the interest from both academic and research communities. Despite the availability of a broad range of face presentation attack detection (PAD) (or countermeasure or antispoofing) schemes, there exists no superior PAD technique due to evolution of sophisticated presentation attacks (or spoof attacks). In this paper, we present a new perspective for face presentation attack detection by introducing light field camera (LFC). Since the use of a LFC can record the direction of each incoming ray in addition to the intensity, it exhibits an unique characteristic of rendering multiple depth (or focus) images in a single capture. Thus, we present a novel approach that involves exploring the variation of the focus between multiple depth (or focus) images rendered by the LFC that in turn can be used to reveal the presentation attacks. To this extent, we first collect a new face artefact database using LFC that comprises of 80 subjects. Face artefacts are generated by simulating two widely used attacks, such as photo print and electronic screen attack. Extensive experiments carried out on the light field face artefact database have revealed the outstanding performance of the proposed PAD scheme when benchmarked with various well established state-of-the-art schemes.

关键词： Biometrics face recognition spoofing security presentation attack detection countermeasure light field camera

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presentation attack detection system for fake Iris: a review

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第10期80卷 15193-15214页

作者： Agarwal, Rohit Jalal, Anand Singh GLA Univ Dept Comp Engn & Applicat Mathura 281406 India

The real-time solicitations of biometric systems have been extensively used for several things with the growing necessities of higher security level. There are numerous biometric traits used for person identification. In recent years, iris biometric trait become very popular and efficient in many security applications. However, biometric systems are prone to presentation attack. This attack is carried out by using spoofing of any biometric modality and present as a real trait. The objective of this paper is to present a broad and well thought-out overview of the effort that has been conceded out over the preceding years in the field of iris anti-spoofing. Spoofing of an iris image can be done by paper print and contact lens etc. So, it is essential to discover fake iris up to its core level. In this survey, we have discussed various research work done in the past on the topic of different classes of presentation attack and summarized the state-of-the-art methods in a structural way. We have also discussed the future direction in the field of iris liveness detection.

关键词： Biometric recognition Iris detection presentation attack detection Anti-spoofing

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presentation attack detection based on score level fusion and challenge-response technique

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JOURNAL OF SUPERCOMPUTING 2021年第5期77卷 4681-4697页

作者： Chou, Chao-Lung Natl Def Univ Chung Cheng Inst Technol Dept Comp Sci & Informat Engn Taoyuan Taoyuan County Taiwan

Biometrics is the state of the art in dealing with identity identification and verification based on the physical and behavioral characteristics and widely used in the fields of Fintech, such as mobile payment and online banking due to its security and convenience. However, there are various attacks against the biometrics system. The presentation attack is one of the most common attacks that an imposter presents fake biometrics to the sensor trying to fool the system. This paper proposes a multimodal presentation attack detection (PAD) method against photo-attack and video-attack in face recognition system by using score level fusion and challenge-response scenario. The proposed challenge-response scenario is that requesting the user to speak out the randomly prompted words. Then, the recognized speech text and the user's mouth motion are detected simultaneously to verify if the user is liveness. Two weighted score level fusion rules, namely weighted sum and weight product, are used to combine the speech and mouth motion traits as a matching score. The final score is fed into supervised machine learning algorithms and trained for classifying spoofing. The experiments are conducted in the self-built database. Experimental results show that the proposed method can achieve the best half total error rate at 3.64% and can effectively improve facial recognition system security.

关键词： Biometrics presentation attack detection Score level fusion Challenge-response

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presentation attack detection and biometric recognition in a challenge-response formalism

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EURASIP JOURNAL ON INFORMATION SECURITY 2022年第1期2022卷 1-15页

作者： Haasnoot, Erwin Spreeuwers, Luuk J. Veldhuis, Raymond N. J. Univ Twente Fac EEMCS Data Sci Grp Enschede Netherlands

presentation attack detection (PAD) is used to mitigate the dangers of the weakest link problem in biometric recognition, in which failure modes of one application affect the security of all other applications. Strong PAD methods are therefore a must, and we believe biometric challenge-response protocols (BCRP) form an underestimated part of this ecosystem. In this paper, we conceptualize what BCRPs are, and we propose a descriptive formalism and categorization for working with them. We validate the categorization against existing literature that we classified to be describing BCRPs. Lastly, we discuss how strong BCRPs provide advantages over PAD methods, specifically in the protection of individual applications and the protection of other applications from inadvertent leaks in BCRP applications. We note that research in BCRPs is fragmented, and our intent for the proposed formalism and categorization are to give focus and direction to research efforts into biometric challenge-response protocols.

关键词： Biometric challenge-response Interactive biometrics presentation attack detection Biometric recognition

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presentation attack detection for Smartphone Based Fingerphoto Recognition Using Second Order Local Structures 14

Presentation Attack Detection for Smartphone Based Fingerpho...

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14th International Conference on Signal Image Technology & Internet Based Systems (SITIS)

作者： Wasnik, Pankaj Ramachandra, Raghavendra Raja, Kiran Busch, Christoph NTNU Norwegian Biometr Lab Gjovik Norway

ISBN: (纸本)9781538693858

Fingerprint recognition on smartphone provides a good alternative over traditional security measures such as lock patterns and pin. However, such fingerprint systems have some inherent problems such as the fact that user will leave their latent fingerprint on the sensor, and the limited sensor area. Additionally, fingerprint sensors impact on the cost and form factor of the device. Hence, in literature, the camera based approaches such as fingerphoto recognition systems got the attention of many researchers and manufacturers. However, such systems are highly vulnerable to presentation attacks such as photo-prints, display and replay attacks. To countermeasure these attacks, we propose a robust presentation attack detection scheme based on the features extracted from the maximum response images obtained from the convolution of second order Gaussian derivatives and the input images at multiple scales. The proposed scheme has achieved the detection performance of BPCER of 1.8%, 0.0% and 0.66% at APCER=10% for the presentation attack instrument species i.e., print-photo, display and replay attacks respectively.

关键词： Fingerphoto verification presentation attack detection Spoof detection Smartphone biometrics Vesselness Filter Image Enhancement

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presentation attack detection for Mobile Device-Based Iris Recognition

Presentation Attack Detection for Mobile Device-Based Iris R...

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11th International Image Processing and Communications Conference (IP and C)

作者： Bartuzi, Ewelina Trokielewicz, Mateusz Res & Acad Comp Network NASK Kolska 12 PL-01045 Warsaw Poland

ISBN: (纸本)9783030312541;9783030312534

Apart from ensuring high recognition accuracy, one of the main challenges associated with mobile iris recognition is reliable presentation attack detection (PAD). This paper proposes a method of detecting presentation attacks when the iris image is collected in visible light using mobile devices. We extended the existing database of 909 bonafide iris images acquired with a mobile phone by collecting additional 900 images of irises presented on a color screen. We explore different image channels in both RGB and HSV color spaces, deep learning-based and geometric model-based image segmentation, and use Local Binary Patterns (LBP) along with the selected statistical images features classified by the Support Vector Machine to propose an iris PAD algorithm suitable for mobile iris recognition setups. We found that the red channel in the RGB color space offers the best-quality input samples from the PAD point of view. In subject-disjoint experiments, this method was able to detect 99.78% of screen presentations, and did not reject any live sample.

关键词： Biometrics Iris recognition presentation attack detection Mobile devices

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presentation attack detection in Face Biometric Systems Using Raw Sensor Data from Smartphones 12

Presentation Attack Detection in Face Biometric Systems Usin...

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12th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS)

作者： Wasnik, Pankaj Raja, Kiran B. Raghavendra, R. Busch, Christoph NTNU Gjovik Norwegian Biometr Lab Gjovik Norway

ISBN: (纸本)9781509056989

Applicability of the face recognition for smartphone-based authentication applications is increasing for different domains such as banking and e-commerce. The unsupervised data capture of face characteristics in biometric applications on smartphones presents the vulnerability to attack the systems using artefact samples. The threat of presentation attacks (a.k.a spoofing attacks) need to be handled to enhance the security of the biometric system. In this work, we present a new approach of using the raw sensor data. We first obtain the residual image corresponding to noise by subtracting the median filtered version of raw data and then computing simple energy value to detect the artefact based presentations. The presented approach uses simple threshold and thereby overcomes the need for learning complex classifiers which are challenging to work on unseen attacks. The proposed method is evaluated using a newly collected database of 390 live presentation attempts of face characteristics and 1530 attack presentations consisting of electronic screen attacks and printed attacks on the iPhone 6S smartphone. Significantly lower average classification error (< 3%) achieved demonstrates the applicability of proposed approach for detecting the presentation attacks.

关键词： presentation attack detection Spoofing Face recognition Smartphones Sensor data

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