Detecting morphed face images

Raghavendra, R.; Raja, Kiran B.; Busch, Christoph

doi:10.1109/btas.2016.7791169

Cited by 119 publications

(113 citation statements)

References 10 publications

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“…Given the residual image, we extract the deep textural features computed using a pre-trained off-the-shelf AlexNet. We have used the features from fully connected layer f c6 to compute the feature from the residual noise 1 https://www.imageclef.org/photodata images. These computed features are then classified using a Probabilistic Collaborative Representation Classifier (P-CRC) [32].…”

Section: Feature Extraction and Detectionmentioning

confidence: 99%

“…The morphed face image generated using the face image from an attacker and a accomplice can easily be verified against both contributing subjects with existing commercial FRS. Also a human expert such as a trained border guard can be confused [14,15,1,16,17,18,19,20]. This scenario becomes critical, when attackers intentionally morph their face image with a non-blacklisted subject, in order to gain access to a protected/secured area.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Feature Extraction and Detectionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Creation of good datasets with morphed face images is one of the most important steps in the development of reliable face morphing detection methods. In [5] 450 morphed faces are created manually from a database comprised of 110 subjects. The face region is detected with Viola Jones detection.…”

Section: Bona Fide Presentation Classification Error Rate (Bpcer) Promentioning

confidence: 99%

“…This means a morph in the training set may share a contributing subject with a morph in the test or validation set. In [7] the experiments from [5] are repeated, but instead the morphing detection process at a passport control is simulated by printing and scanning the face images. Morphing attack detection performance was analysed before and after printing and scanning.…”

Section: Bona Fide Presentation Classification Error Rate (Bpcer) Promentioning

confidence: 99%

Towards Robust Evaluation of Face Morphing Detection

Spreeuwers

Schils

Veldhuis

2018

2018 26th European Signal Processing Conference (EUSIPCO)

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Automated face recognition is increasingly used as a reliable means to establish the identity of persons for various purposes, ranging from automated passport checks at the border to transferring money and unlocking mobile phones. Face morphing is a technique to blend facial images of two or more subjects such that the result resembles both subjects. Face morphing attacks pose a serious risk for any face recognition system. Without automated morphing detection, state of the art face recognition systems are extremely vulnerable to morphing attacks. Morphing detection methods published in literature often only work for a few types of morphs or on a single dataset with morphed photographs. We create face morphing databases with varying characteristics and how for a LBP/SVM based morphing detection method that performs on par with the state of the art (around 2% EER), the performance collapses with an EER as high as if it is tested across databases with different characteristics. In addition we show that simple image manipulations like adding noise or rescaling can be used to obscure morphing artifacts and deteriorate the morphing detection performance.

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“…Several researchers become aware of the danger of morphing attacks and developed different forensic methods to detect this kind of fraud. In contrast to the already proposed methods, which are based on image degeneration [4], Binarized Statistical Image Features (BSIF) [5], neural networks [6], [7] or JPEG compression artifacts [8], we propose a method that is based on a physical illumination model. Illumination estimation to detect frauds was already studied in detail by [9], [10] to detect compositions of multiple photographs.…”

Section: Introductionmentioning

confidence: 99%

Reflection Analysis for Face Morphing Attack Detection

Seibold

Hilsmann

Eisert

2018

2018 26th European Signal Processing Conference (EUSIPCO)

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A facial morph is a synthetically created image of a face that looks similar to two different individuals and can even trick biometric facial recognition systems into recognizing both individuals. This attack is known as face morphing attack. The process of creating such a facial morph is well documented and a lot of tutorials and software to create them are freely available. Therefore, it is mandatory to be able to detect this kind of fraud to ensure the integrity of the face as reliable biometric feature. In this work, we study the effects of face morphing on the physically correctness of the illumination. We estimate the direction to the light sources based on specular highlights in the eyes and use them to generate a synthetic map for highlights on the skin. This map is compared with the highlights in the image that is suspected to be a fraud. Morphing faces with different geometries, a bad alignment of the source images or using images with different illuminations, can lead to inconsistencies in reflections that indicate the existence of a morphing attack.

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Detecting morphed face images

Cited by 119 publications

References 10 publications

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

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

Towards Robust Evaluation of Face Morphing Detection

Reflection Analysis for Face Morphing Attack Detection

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