Securing Face Liveness Detection Using Unforgeable Lip Motion Patterns

Zhou, Man; Wang, Qian; Li, Qi; Jiang, Peipei; Yang, Jingxiao; Shen, Chao; Wang, Cong; Ding, Shouhong

doi:10.48550/arxiv.2106.08013

Cited by 3 publications

(6 citation statements)

References 33 publications

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“…where Pre i represents the estimated pressure of the i-th phoneme, m is the correlation coefficient between sonority and pressure, and n is a constant term used to adjust the pressure coordinates to positive values, which is set to 169.85 in our experiments 3 . We construct the sonority hierarchy and the estimated pressure for the speech signal "set an alarm for six am", as illustrated in Fig.…”

Section: Pressure Scale Conversionmentioning

confidence: 99%

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Securing Liveness Detection for Voice Authentication via Pop Noises

Jiang

Wang

Lin

et al. 2023

IEEE Trans. Dependable and Secure Comput.

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Voice authentication has been increasingly adopted for sensitive operations on mobile devices. While voice biometrics can distinguish individuals by their spectral features (such as voiceprints), they are known to be prone to spoofing attacks, where malicious attackers can use pre-recorded or synthesized samples from legitimate users or impersonate the speaking style of the targeted user to deceive the voice authentication system. In this paper, we design and implement a novel software-only anti-spoofing system on smartphones. Our system leverages the pop noise, which is generated by the user's oral airflow when speaking the passphrase close to the microphone. The pop noise is delicate and subject to user diversity, making it hard to be recorded by replay attacks beyond a certain distance or to be imitated precisely by impersonators. Specifically, we design a new pop noise detection scheme to pinpoint pop noises at the phonemic level, based on which we establish a theoretical model to calculate the sound pressure level from the speech signal in order to get the estimated pressure signal, and then analyze the consistency with the actual pressure signal extracted from the pop noise. Furthermore, we calculate the similarity score of the unique sequences which describe the individually unique relationship between pop noises and phonemes to resist spoofing attacks. Our evaluation on a dataset of 30 participants and three smartphones shows that our system achieves over 94.79% accuracy. Our system requires no additional hardware and is robust to various factors including authentication angle, authentication distance, the length of passphrase, ambient noise, etc.

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Section: Pressure Scale Conversionmentioning

confidence: 99%

“…C OMPARED with password-based authentication, biometric authentication [2], [3] is more convenient since it is hands-free, and users do not need to memorize passwords. Compared with other biometric authentication, voice authentication is more low-cost, natural and convenient.…”

Section: Introductionmentioning

confidence: 99%

Securing Liveness Detection for Voice Authentication via Pop Noises

Jiang

Wang

Lin

et al. 2023

IEEE Trans. Dependable and Secure Comput.

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“…Refs. [67,142] utilized acoustic signals to track the lip motion patterns of individuals as they speak their passphrase to authenticate in combination with conventional facial authentication. Refs.…”

Section: Faraj Et Al Utilizes Lip Motion As a Form Of Liveness Detectionmentioning

confidence: 99%

“…Facial [66], fingerprint, and voice authentication have particularly gained attention in recent years. Research has found that many facial authentication systems can be easily fooled by images, projections of images onto 3D heads, and 3D silicone masks [67]. Lip motion authentication comes in two forms to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Advancements in Lip Motion Analysis: A Review

Torrie,

Sumsion,

Lee

et al. 2023

Electronics

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This work reviews the dataset-driven advancements that have occurred in the area of lip motion analysis, particularly visual lip-reading and visual lip motion authentication, in the deep learning era. We provide an analysis of datasets and their usage, creation, and associated challenges. Future research can utilize this work as a guide for selecting appropriate datasets and as a source of insights for creating new and innovative datasets. Large and varied datasets are vital to a successful deep learning system. There have been many incredible advancements made in these fields due to larger datasets. There are indications that even larger, more varied datasets would result in further improvement upon existing systems. We highlight the datasets that brought about the progression in lip-reading systems from digit- to word-level lip-reading, and then from word- to sentence-level lip-reading. Through an in-depth analysis of lip-reading system results, we show that datasets with large amounts of diversity increase results immensely. We then discuss the next step for lip-reading systems to move from sentence- to dialogue-level lip-reading and emphasize that new datasets are required to make this transition possible. We then explore lip motion authentication datasets. While lip motion authentication has been well researched, it is not very unified on a particular implementation, and there is no benchmark dataset to compare the various methods. As was seen in the lip-reading analysis, large, diverse datasets are required to evaluate the robustness and accuracy of new methods attempted by researchers. These large datasets have pushed the work in the visual lip-reading realm. Due to the lack of large, diverse, and publicly accessible datasets, visual lip motion authentication research has struggled to validate results and real-world applications. A new benchmark dataset is required to unify the studies in this area such that they can be compared to previous methods as well as validate new methods more effectively.

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“…Some use the FMCW ultrasonic signal to extract the feature of teeth actions for authentication [20,30], and some use the sound signal to extract the characteristics of the throat movement [31] for authentication. The FMCW ultrasonic signal is also used for lip motion [32] authentication or face liveness [33] detection.…”

Section: Acoustic Authenticationmentioning

confidence: 99%

MetaEar: Imperceptible Acoustic Side Channel Continuous Authentication Based on ERTF

Chang

Wang

et al. 2022

Electronics

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With the development of ubiquitous mobile devices, biometrics authentication has received much attention from researchers. For immersive experiences in AR (augmented reality), convenient continuous biometric authentication technologies are required to provide security for electronic assets and transactions through head-mounted devices. Existing fingerprint or face authentication methods are vulnerable to spoof attacks and replay attacks. In this paper, we propose MetaEar, which harnesses head-mounted devices to send FMCW (Frequency-Modulated Continuous Wave) ultrasonic signals for continuous biometric authentication of the human ear. CIR (channel impulse response) leveraged the channel estimation theory to model the physiological structure of the human ear, called the Ear Related Transfer Function (ERTF). It extracts unique representations of the human ear’s intrinsic and extrinsic biometric features. To overcome the data dependency of Deep Learning and improve its deployability in mobile devices, we use the lightweight learning approach for classification and authentication. Our implementation and evaluation show that the average accuracy can reach about 96% in different scenarios with small amounts of data. MetaEar enables one to handle immersive deployable authentication and be more sensitive to replay and impersonation attacks.

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Securing Face Liveness Detection Using Unforgeable Lip Motion Patterns

Cited by 3 publications

References 33 publications

Securing Liveness Detection for Voice Authentication via Pop Noises

Securing Liveness Detection for Voice Authentication via Pop Noises

Data-Driven Advancements in Lip Motion Analysis: A Review

MetaEar: Imperceptible Acoustic Side Channel Continuous Authentication Based on ERTF

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