FD-GAN: Face De-Morphing Generative Adversarial Network for Restoring Accomplice’s Facial Image

Peng, Fei; Zhang, Le-Bing; Long, Min

doi:10.1109/access.2019.2920713

Cited by 60 publications

(59 citation statements)

References 25 publications

(66 reference statements)

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“…In [13], [14] the so-called demorphing algorithm was proposed, an approach where reversion is done explicitly. In addition to the explicit demorphing approach, demorphing based on a Generative Adversarial Network (GAN) is proposed in [16].…”

Section: B Differential Madmentioning

confidence: 99%

Deep Face Representations for Differential Morphing Attack Detection

Scherhag

Rathgeb

Merkle³

et al. 2020

IEEE Trans.Inform.Forensic Secur.

127

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The vulnerability of facial recognition systems to face morphing attacks is well known. Many different approaches for morphing attack detection (MAD) have been proposed in the scientific literature. However, the MAD algorithms proposed so far have mostly been trained and tested on datasets whose distributions of image characteristics are either very limited (e.g., only created with a single morphing tool) or rather unrealistic (e.g., no print-scan transformation). As a consequence, these methods easily overfit on certain image types and the results presented cannot be expected to apply to real-world scenarios. For example, the results of the latest NIST FRVT MORPH show that the majority of submitted MAD algorithms lacks robustness and performance when considering unseen and challenging datasets. In this work, subsets of the FERET and FRGCv2 face databases are used to create a realistic database for training and testing of MAD algorithms, containing a large number of ICAO-compliant bona fide facial images, corresponding unconstrained probe images, and morphed images created with four different face morphing tools. Furthermore, multiple post-processings are applied on the reference images, e.g., print-scan and JPEG2000 compression. On this database, previously proposed differential morphing algorithms are evaluated and compared. In addition, the application of deep face representations for differential MAD algorithms is investigated. It is shown that algorithms based on deep face representations can achieve very high detection performance (less than 3% D-EER) and robustness with respect to various post-processings. Finally, the limitations of the developed methods are analyzed.

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Section: B Differential Madmentioning

confidence: 99%

Deep Face Representations for Differential Morphing Attack Detection

Scherhag

Rathgeb

Merkle³

et al. 2020

IEEE Trans.Inform.Forensic Secur.

127

View full text Add to dashboard Cite

show abstract

“…In this paper, a novel method to detect morphing attacks is explained using a reverse de-morphing approach based on convolutional neural networks. There are several differences compared with previous works [23], [24], which are explained as follows.…”

Section: Introductionmentioning

confidence: 93%

“…The morphed picture and another one are used. This is the typical scenario in ABC systems [10], [23], [24], [28]. The first approach, no-reference, seeks to determine the noise or the deterioration in terms of quality of the image.…”

Section: Previous Workmentioning

confidence: 99%

“…Finally, this work is based on Delaunay-Voronoi triangulation but there are new approaches in which the demorphing process is performed with neural networks. For instance, Damer et al [25] and Peng et al [24] propose the use of the generative adversarial network (GAN). Regarding Peng et al's work, it is based on disentangling the accomplice identity from a potentially morphed image.…”

Section: Introductionmentioning

confidence: 99%

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Border Control Morphing Attack Detection with a Convolutional Neural Network De-morphing Approach

et al. 2020

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Currently, the use of biometric identification, automated or semiautomated, is a reality. For this reason, the number of attacks has increased in such systems. One of the most common biometric attacks is the presentation attack (PA) because it is relatively easy to perform. Automated border control (ABC) is a clear target for phishers. Concerning biometric attacks, morphing is one of the most threatening attacks because authentication systems are usually unable to correctly detect them. In this attack, a fake face is generated with the morphing and blending of two different subjects (genuine and phisher), and the image result is stored in the passport. These attacks can generate risky situations in cases of border crossings where an ABC system should perform identification tasks. This research work proposes a de-morphing architecture that is founded on a convolutional neural network (CNN) architecture. This technique is based on the use of two images: the potentially morphed image stored in the passport, and the snapshot of the person located in the ABC system. The goal of the de-morphing process is to unravel the chip image. If the chip image is a morphed one, the revealing process between the in vivo image and the morphed chip image will return a different facial identity to the person located in the ABC system, and the impostor will be uncovered in situ. If the chip image is a non-morphing image, the resulting image will be similar to a genuine passenger. Therefore, the information obtained is considered at the border crossing. The equal error rate (EER) achieved is very low compared to the literature values published to date. The accomplished outcomes endorse a robust method that provides high accuracy rates without taking into account the quality of images used. This key point is crucial to plausible deployment plans in areas such as ABC. INDEX TERMS ABC, biometric systems, de-morphing, neural networks, MAD.

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“…In (14) illustrated graph based approach for face recognition where a reference face graph (RFG) technique was used to recognize unknown face from the database. Whereas some researchers have suggested deep learning and neural network approaches for face recognition (15)(16)(17)(18)(19) 3 Proposed scheme…”

Section: Literature Reviewmentioning

confidence: 99%

Texture based FACE recognition using GLCM and LBP schemes

Kumar¹

2020

IJST

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Objectives: Automatic face recognition has been an important area of biometric authentication and verification system in various applications including crime detection, access control, video surveillance, tracking service and other related area. Methods/Statistical analysis: In this study, we present Grey Level Co-occurrence Matrix (GLCM) over Local Binary Patterns (LBP) named as GOL texture feature technique for face classification. The experiments have been conducted on AT & T Cambridge Laboratory face images also called (ORL-faces) and Georgia Tech (GT-faces) databases respectively. Findings: We performed comparative analysis of GLCM and LBP method separately and results showed that proposed GOL method outperformed in terms of average sensitivity, average specificity, and retrieval time. These findings show efficacy of our proposed system.

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FD-GAN: Face De-Morphing Generative Adversarial Network for Restoring Accomplice’s Facial Image

Cited by 60 publications

References 25 publications

Deep Face Representations for Differential Morphing Attack Detection

Deep Face Representations for Differential Morphing Attack Detection

Border Control Morphing Attack Detection with a Convolutional Neural Network De-morphing Approach

Texture based FACE recognition using GLCM and LBP schemes

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