Adversarial attacks on fingerprint liveness detection

Fei, Jianwei; Xia, Zhihua; Yu, Peipeng; Xiao, Fengjun

doi:10.1186/s13640-020-0490-z

Cited by 42 publications

(9 citation statements)

References 29 publications

(32 reference statements)

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“…We have tested existing adversarial methods in this study, including FGSM, MI-FGSM, Deepfool, L-BFGS, C&W, BIM, Foolbox, PGD, and JSMA [16]. Our goal is to compromise existing DL algorithms so that each recognition system misclassifies data with the fewest number of perturbations.…”

Section: Test Resultsmentioning

confidence: 99%

Adversarial Examples—Security Threats to COVID-19 Deep Learning Systems in Medical IoT Devices

Rahman

Hossain

Alrajeh

et al. 2021

IEEE Internet Things J.

151

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Medical IoT devices are rapidly becoming part of management ecosystems for pandemics such as COVID-19. Existing research shows that deep learning (DL) algorithms have been successfully used by researchers to identify COVID-19 phenomena from raw data obtained from medical IoT devices. Some examples of IoT technology are radiological media such as CT scanning and X-ray images, body temperature measurement using thermal cameras, safe social distancing identification using live face detection, and face mask detection from camera images. However, researchers have identified several security vulnerabilities in DL algorithms to adversarial perturbations. In this paper, we have tested a number of COVID-19 diagnostic methods that rely on DL algorithms with relevant adversarial examples. Our test results show that DL models that do not consider defensive models against adversarial perturbations remain vulnerable to adversarial attacks. Finally, we present in detail the adversarial example generation process, implementation of the attack model, and the perturbations of existing DL-based COVID-19 diagnostic applications. We hope that this work will raise awareness of adversarial attacks and encourages others to safeguard DL models from attacks on healthcare systems.

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Section: Test Resultsmentioning

confidence: 99%

Adversarial Examples—Security Threats to COVID-19 Deep Learning Systems in Medical IoT Devices

Rahman

Hossain

Alrajeh

et al. 2021

IEEE Internet Things J.

151

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show abstract

“…This research area may be investigated more to promote research in the diversity of human personalities. Although DL models are robust and produce state-of-the-art performances, but they are still susceptible to security challenges/attacks such as adversarial attacks, spoofing attacks, and presentation attacks [6,109,114,123,124]. For security reason, fingerprint biometric systems don't store fingerprint features as they are but save them as template which is considered relatively safe.…”

Section: Open Challenges and Prospect For Future Researchmentioning

confidence: 99%

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

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Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.

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“…This problem is particularly critic in security-related domains leveraging CNNs in one of their stages. Focusing on the case of fingerprints, the vulnerability of FPAD systems to adversarial perturbations has already been demonstrated [5], [6], describing methods designed to generate perturbed fingerprint images able to mislead a target FPAD in the digital domain. In both cases, the attacks assume that the attacker can feed the perturbed digital image directly into the CNN.…”

Section: Cnn-based Fpad and Adversarial Perturbationsmentioning

confidence: 99%

“…Adversarial perturbations are thus potentially very dangerous since an attacker could intentionally add small perturbations to an image, keeping it visually unchanged, to force the classifier's decision. On this line, some recent works already demonstrated the effectiveness of a digital attack on a CNN FPAD [5], [6]. This type of attack assumes the attackers to be able to enter the communication channel between the sensor and the neural network, making them able to submit the adversarial images as input to the FPAD.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition. ICPR International Workshops and Challenges

2021

Lecture Notes in Computer Science

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With the advent of the deep learning era, Fingerprint-based Authentication Systems (FAS) equipped with Fingerprint Presentation Attack Detection (FPAD) modules managed to avoid attacks on the sensor through artificial replicas of fingerprints. Previous works highlighted the vulnerability of FPADs to digital adversarial attacks. However, in a realistic scenario, the attackers may not have the possibility to directly feed a digitally perturbed image to the deep learning based FPAD, since the channel between the sensor and the FPAD is usually protected. In this paper we thus investigate the threat level associated with adversarial attacks against FPADs in the physical domain. By materially realising fakes from the adversarial images we were able to insert them into the system directly from the "exposed" part, the sensor. To the best of our knowledge, this represents the first proof-of-concept of a fingerprint adversarial presentation attack. We evaluated how much liveness score changed by feeding the system with the attacks using digital and printed adversarial images. To measure what portion of this increase is due to the printing itself, we also re-printed the original spoof images, without injecting any perturbation. Experiments conducted on the LivDet 2015 dataset demonstrate that the printed adversarial images achieve ∼ 100% attack success rate against an FPAD if the attacker has the ability to make multiple attacks on the sensor (10) and a fairly good result (∼ 28%) in a one-shot scenario. Despite this work must be considered as a proof-of-concept, it constitutes a promising pioneering attempt confirming that an adversarial presentation attack is feasible and dangerous.

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Adversarial attacks on fingerprint liveness detection

Cited by 42 publications

References 29 publications

Adversarial Examples—Security Threats to COVID-19 Deep Learning Systems in Medical IoT Devices

Adversarial Examples—Security Threats to COVID-19 Deep Learning Systems in Medical IoT Devices

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Pattern Recognition. ICPR International Workshops and Challenges

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