Preventing face morphing attacks by using legacy face images

Abstract Countries allow citizens to upload a face image or provide printed copies to authorities to issue their passport. This allows prior image manipulation with criminal intent. A composite image can be created by blending the images of two individuals before submitting the composite image to the authorities. Depending on several factors, the submitted morphed face image can fool the issuing officer to issue a legitimate document. The document can then be successfully used by either contributor to attack the automatic Face Recognition Systems (FRS) operating, for example, at Automatic Border Control (ABC) airport gates. This is known as a Morphing Attack (MA), an identity sharing scheme with serious consequences. Here, the security vulnerabilities due to MAs are identified and analysed, and an additional security measure that allows mitigating the risk or preventing MAs in certain scenarios is proposed. The measure introduces more comparisons by keeping the old passport or ID card image in the chip, in passport renewal applications or first time passport applications, respectively. This approach is implemented with two FRSs on a challenging dataset and the dramatic decrease in the vulnerability is shown. Finally, their performance is compared with a state‐of‐the‐art MA detection algorithm on the same dataset

Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking

Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can be tested on unseen data in an effort to better generalize. The newly constructed dataset consists of facial images from 150 subjects from various ethnicities, age-groups and both genders. In order to challenge the existing MAD algorithms, the morphed images are with careful subject pre-selection created from the contributing images, and further post-processed to remove morphing artifacts. The images are also printed and scanned to remove all digital cues and to simulate a realistic challenge for MAD algorithms. Further, we present a new online evaluation platform to test algorithms on sequestered data. With the platform we can benchmark the morph detection performance and study the generalization ability. This work also presents a detailed analysis on various subsets of sequestered data and outlines open challenges for future directions in MAD research.Comment: This paper is a pre-print. The article is accepted for publication in IEEE Transactions on Information Forensics and Security (TIFS

Visualizing landmark based face morphing traces on digital images

This paper focuses on an identity sharing scheme known as face image morphing or simply morphing. Morphing is the process of creating a composite face image, a morph, by digitally manipulating face images of different individuals, usually two. Under certain circumstances, the composite image looks like both contributors and can be used by one of them (accomplice) to issue an ID document. The other contributor (criminal) can then use the ID document for illegal activities, which is a serious security vulnerability. So far, researchers have focused on automated morphing detection solutions. Our main contribution is the evaluation of the effectiveness and limitations of two image forensics methods in visualizing morphing related traces in digital images. Visualization of morphing traces is important as it can be used as hard evidence in forensic context (i.e., court cases) and lead to the development of morphing algorithm specific feature extraction strategies for automated detection. To evaluate the two methods, we created morphs using two state-of-the-art morphing algorithms, complying with the face image requirements of three currently existing online passport application processes. We found that complementary use of the visualization methods can reveal morphing related traces. We also show how some application process-specific requirements affect visualization results by testing three likely morphing attack scenarios with varied image processing parameters and propose application process amendments that would make forensic image analysis more reliable

Morphing Attack Detection - Database, Evaluation Platform and Benchmarking

Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can be tested on unseen data in an effort to better generalize. The newly constructed dataset consists of facial images from 150 subjects from various ethnicities, age-groups and both genders. In order to challenge the existing MAD algorithms, the morphed images are with careful subject pre-selection created from the contributing images, and further post-processed to remove morphing artifacts. The images are also printed and scanned to remove all digital cues and to simulate a realistic challenge for MAD algorithms. Further, we present a new online evaluation platform to test algorithms on sequestered data. With the platform we can benchmark the morph detection performance and study the generalization ability. This work also presents a detailed analysis on various subsets of sequestered data and outlines open challenges for future directions in MAD research

Morphing Attack Detection-Database, Evaluation Platform, and Benchmarking

Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can be tested on unseen data in an effort to better generalize. The newly constructed dataset consists of facial images from 150 subjects from various ethnicities, age-groups and both genders. In order to challenge the existing MAD algorithms, the morphed images are with careful subject pre-selection created from the contributing images, and further post-processed to remove morphing artifacts. The images are also printed and scanned to remove all digital cues and to simulate a realistic challenge for MAD algorithms. Further, we present a new online evaluation platform to test algorithms on sequestered data. With the platform we can benchmark the morph detection performance and study the generalization ability. This work also presents a detailed analysis on various subsets of sequestered data and outlines open challenges for future directions in MAD research