Face Morphing Detection Using Fourier Spectrum of Sensor Pattern Noise

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

doi:10.1109/icme.2018.8486607

Cited by 50 publications

(49 citation statements)

References 16 publications

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“…Subsequently, the mean and variance are derived from the resulting histogram. A very similar approach was presented in [86]. Recently, an improved version of this scheme based on PRNU variance analysis across image blocks was proposed in [85].…”

Section: ) No-reference Morphing Attack Detectionmentioning

confidence: 99%

Face Recognition Systems Under Morphing Attacks: A Survey

et al. 2019

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Recently, researchers found that the intended generalizability of (deep) face recognition systems increases their vulnerability against attacks. In particular, the attacks based on morphed face images pose a severe security risk to face recognition systems. In the last few years, the topic of (face) image morphing and automated morphing attack detection has sparked the interest of several research laboratories working in the field of biometrics and many different approaches have been published. In this paper, a conceptual categorization and metrics for an evaluation of such methods are presented, followed by a comprehensive survey of relevant publications. In addition, technical considerations and tradeoffs of the surveyed methods are discussed along with open issues and challenges in the field. INDEX TERMS Biometrics, face morphing attack, face recognition, image morphing, morphing attack detection.

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Section: ) No-reference Morphing Attack Detectionmentioning

confidence: 99%

Face Recognition Systems Under Morphing Attacks: A Survey

et al. 2019

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“…2. In previous works [18][19][20][21], different statistics of spatial and spectral features extracted from the PRNU patterns of face images have been analysed for the task of morphing attack detection. The proposed detection system builds upon these works extending them by two major additions: on the one hand, a greater variety of spatial and spectral features is analysed to maximise the extracted information; on the other hand, to achieve high robustness and increased detection accuracy, a fusion of retouching detection scores obtained from each of the spatial and spectral features is performed.…”

Section: Fig 2 Example Applications Of Selected Beautification Apps mentioning

confidence: 99%

“…An overview of extracted features is given in Table 4. Types of extracted features are partly inspired by previous works [18][19][20][21]. In [21] it has been shown that these features exhibit high generalisability across cameras.…”

Section: Feature Extractionmentioning

confidence: 99%

“…• A facial retouching detection system is proposed, which analyses spatial and spectral features extracted from photo response non-uniformity (PRNU) patterns across image regions. The presented scheme builds upon the works of Debiasi et al [18,19], Zhang et al [20] and Scherhag et al [21] in which an analysis of the PRNU pattern was successfully utilised to detect image manipulations resulting from face morphing [13]. These approaches are adapted and extended by a normalized scorelevel fusion of equally weighted detection scores obtained from the analysis of various spatial and spectral features of the PRNU.…”

Section: Introductionmentioning

confidence: 99%

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PRNU‐based detection of facial retouching

et al. 2020

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Nowadays, many facial images are acquired using smart phones. To ensure the best outcome, users frequently retouch these images before sharing them, e.g. via social media. Modifications resulting from used retouching algorithms might be a challenge for face recognition technologies. Towards deploying robust face recognition as well as enforcing anti-photoshop legislations, a reliable detection of retouched face images is needed. In this work, the effects of facial retouching on face recognition are investigated. A qualitative assessment of 32 beautification apps is conducted. Based on this assessment five apps are chosen which are used to create a database of 800 beautified face images. Biometric performance is measured before and after retouching using a commercial face recognition system. Subsequently, a retouching detection system based on the analysis of photo response non-uniformity (PRNU) is presented. Specifically, scores obtained from analysing spatial and spectral features extracted from PRNU patterns across image cells are fused. In a scenario, in which unaltered bona fide images are compressed to the average sizes of the retouched images using JPEG, the proposed PRNU-based detection scheme is shown to robustly distinguish between bona fide and retouched images achieving an average detection equal error rate of 13.7% across all retouching algorithms. Fig. 1 Application of a beautification app: (a) original image, (b) retouched image, and (c) main differences between (a) and (b) (a) Original, (b) Retouched, (c) Differences

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“…In addition to the structural descriptor and texture analysis, other studies assess the degradation of the image through spectral image analysis. Some researchers try to detect a possible manipulation using the last mentioned technique [35]. Others try to evaluate the noise pattern employing the photo response non-uniformity (PRNU) approach [50]) in the full image [32] or each region [33].…”

Section: Previous Workmentioning

confidence: 99%

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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Face Morphing Detection Using Fourier Spectrum of Sensor Pattern Noise

Cited by 50 publications

References 16 publications

Face Recognition Systems Under Morphing Attacks: A Survey

Face Recognition Systems Under Morphing Attacks: A Survey

PRNU‐based detection of facial retouching

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

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