PRNU Variance Analysis for Morphed Face Image Detection

Debiasi, Luca; Rathgeb, Christian; Scherhag, Ulrich; Uhl, Andreas; Busch, Christoph

doi:10.1109/btas.2018.8698576

Cited by 32 publications

(39 citation statements)

References 26 publications

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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%

“…• 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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Section: Fig 2 Example Applications Of Selected Beautification Apps mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PRNU‐based detection of facial retouching

et al. 2020

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“…From a substantive viewpoint, morphing's corpora are designed with open source and well-known software such as the GNU Image Manipulation Program (GIMP) which has a plugin called the GIMP Animation Package (GAP) [26]. This plugin is able to merge images [10], [13], [23], [27], but most of the software uses the Delaunay-Voronoi triangulation algorithm (DVT) [28]- [33] and a swapping technique to improve the outcome achieved [34]- [39]. Moreover, some current research works use morphing pictures with generative adversarial networks (GANs) instead of using the triangulation process as mentioned previously [25].…”

Section: Previous Workmentioning

confidence: 99%

“…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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“…Further, the combination of deep features with handcrafted features is proposed in [10]. Recently, the spectral analysis of Photo Response Non-Uniformity (PRNU) has been em-ployed [8] [22], to analyse modifications caused by the morphing procedure. For a quick overview of the existing state of the art based on morph attack detection are presented in [23].…”

Section: Related Workmentioning

confidence: 99%

Detecting Morphed Face Attacks Using Residual Noise from Deep Multi-scale Context Aggregation Network

Venkatesh¹,

Ramachandra²,

Raja³

et al. 2020

Preprint

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Along with the deployment of the Face Recognition Systems (FRS), concerns were raised related to the vulnerability of those systems towards various attacks including morphed attacks. The morphed face attack involves two different face images in order to obtain via a morphing process a resulting attack image, which is sufficiently similar to both contributing data subjects. The obtained morphed image can successfully be verified against both subjects visually (by a human expert) and by a commercial FRS. The face morphing attack poses a severe security risk to the e-passport issuance process and to applications like border control, unless such attacks are detected and mitigated. In this work, we propose a new method to reliably detect a morphed face attack using a newly designed denoising framework. To this end, we design and introduce a new deep Multi-scale Context Aggregation Network (MS-CAN) to obtain denoised images, which is subsequently used to determine if an image is morphed or not. Extensive experiments are carried out on three different morphed face image datasets. The Morphing Attack Detection (MAD) performance of the proposed method is also benchmarked against 14 different state-of-the-art techniques using the ISO-IEC 30107-3 evaluation metrics. Based on the obtained quantitative results, the proposed method has indicated the best performance on all three datasets and also on cross-dataset experiments.

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PRNU Variance Analysis for Morphed Face Image Detection

Cited by 32 publications

References 26 publications

PRNU‐based detection of facial retouching

PRNU‐based detection of facial retouching

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

Detecting Morphed Face Attacks Using Residual Noise from Deep Multi-scale Context Aggregation Network

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