Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing

Stehouwer, Joel; Jourabloo, Amin; Liu, Yaojie; Liu, Xiaoming

doi:10.1109/cvpr42600.2020.00732

Cited by 34 publications

(21 citation statements)

References 30 publications

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“…Therefore, if features are naïvely extracted from images without taking the SiFs into account, then the extracted features may be located in accordance with the SiFs, and deterioration of face anti-spoofing accuracy would be yielded (Figure 1). Some methods [11], [12] have been proposed to increase generalization ability by alleviating the effects of SiFs. [11] proposed a method to train a model to achieve invariance to noise patterns incurred by sensory devices.…”

Section: Introductionmentioning

confidence: 99%

“…Some methods [11], [12] have been proposed to increase generalization ability by alleviating the effects of SiFs. [11] proposed a method to train a model to achieve invariance to noise patterns incurred by sensory devices. [12] attempted to make a model to be invariant to identities by explicitly disentangling identity features using identity labels.…”

Section: Introductionmentioning

confidence: 99%

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Suppressing Spoof-Irrelevant Factors for Domain-Agnostic Face Anti-Spoofing

Kim¹,

Kim²

2021

IEEE Access

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Face anti-spoofing aims to prevent false authentications of face recognition systems by distinguishing whether an image is originated from a human face or a spoof medium. In this work, we note that images from unseen domains having different spoof-irrelevant factors (e.g., background patterns and subject) induce domain shift between source and target distributions. Also, when the same SiFs are shared by the spoof and genuine images, they show a higher level of visual similarity and this hinders accurate face anti-spoofing. Hence, we aim to minimize the discrepancies among different domains via alleviating the effects of SiFs, and achieve improvements in generalization to unseen domains. To realize our goal, we propose a novel method called a Doubly Adversarial Suppression Network (DASN) that is trained to neglect the irrelevant factors and to focus more on faithful task-relevant factors. Our DASN consists of two types of adversarial learning schemes. In the first adversarial learning scheme, multiple SiFs are suppressed by deploying multiple discrimination heads that are trained against an encoder. In the second adversarial learning scheme, each of the discrimination heads is also adversarially trained to suppress a spoof factor, and the group of the secondary spoof classifier and the encoder aims to intensify the spoof factor by overcoming the suppression. We evaluate the proposed method on four public benchmark datasets, and achieve remarkable evaluation results in generalizing to unseen domains. The results demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Suppressing Spoof-Irrelevant Factors for Domain-Agnostic Face Anti-Spoofing

Kim¹,

Kim²

2021

IEEE Access

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“…Recent advances in deep learning have dramatically improved recognition accuracy. However, they are vulnerable to spoofing attacks [1,2]. A spoof could be a printed photo of the actual object (print attack), a replayed digital video (replay attack), a fake object, such as an artificial flower (fake attack), etc.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we address unsupervised spoof detection in contrast to existing RGB-based anti-spoofing approaches that rely on spoof supervision [1,2,7,8]. Unsupervised spoof detection can be a task to detect a spoof as a sample outside the training distribution of live samples.…”

Section: Introductionmentioning

confidence: 99%

A Hyperspectral Approach For Unsupervised Spoof Detection With Intra-Sample Distribution

Kaichi

Ozasa

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Despite the high recognition accuracy of recent deep neural networks, they can be easily deceived by spoofing. Spoofs (e.g., a printed photograph) visually resemble the actual objects quite closely. Thus, we propose a method for spoof detection with a hyperspectral image (HSI) that can effectively detect differences in surface materials. In contrast to existing anti-spoofing approaches, the proposed method learns the feature representation for spoof detection without spoof supervision. The informative pixels on an HSI are embedded onto the feature space, and we identify the spoof from their distribution. As this is the first attempt at unsupervised spoof detection with an HSI, a new dataset that includes spoofs, named Hyperspectral Spoof Dataset (HSSD), has been developed. The experimental results indicate that the proposed method performs significantly better than the baselines. The source code and the dataset are available on Github. 1

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“…In the last few years, there has been an increase in control access based on the biometric system using unique personal biometric information from a human [1]. The main reasons for developing a biometric system are security breaches or false transactions in non-biometric systems, which tend to be cracked due to certain weaknesses.…”

Section: Introductionmentioning

confidence: 99%

A Review on Face Anti-Spoofing

Perdana

Ardiyanto

Nugroho

2021

IJITEE

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The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types.

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Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing

Cited by 34 publications

References 30 publications

Suppressing Spoof-Irrelevant Factors for Domain-Agnostic Face Anti-Spoofing

Suppressing Spoof-Irrelevant Factors for Domain-Agnostic Face Anti-Spoofing

A Hyperspectral Approach For Unsupervised Spoof Detection With Intra-Sample Distribution

A Review on Face Anti-Spoofing

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