10 research outputs found
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 stateâofâtheâart 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
Abundant toxin-related genes in the genomes of beneficial symbionts from deep-sea hydrothermal vent mussels
Bathymodiolus mussels live in symbiosis with intracellular sulfur-oxidizing (SOX) bacteria that provide them with nutrition. We sequenced the SOX symbiont genomes from two Bathymodiolus species. Comparison of these symbiont genomes with those of their closest relatives revealed that the symbionts have undergone genome rearrangements, and up to 35% of their genes may have been acquired by horizontal gene transfer. Many of the genes specific to the symbionts were homologs of virulence genes. We discovered an abundant and diverse array of genes similar to insecticidal toxins of nematode and aphid symbionts, and toxins of pathogens such as Yersinia and Vibrio. Transcriptomics and proteomics revealed that the SOX symbionts express the toxin-related genes (TRGs) in their hosts. We hypothesize that the symbionts use these TRGs in beneficial interactions with their host, including protection against parasites. This would explain why a mutualistic symbiont would contain such a remarkable 'arsenal' of TRG
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
Forging Ahead By Land and By Sea: Archaeology and Paleoclimate Reconstruction in Madagascar
Madagascar is an exceptional example of island biogeography. Though a large island, Madagascarâs landmass is small relative to other places in the world with comparable levels of biodiversity, endemicity, and topographic and climatic variation. Moreover, the timing of Madagascarâs human colonization and the social-ecological trajectories that followed human arrival make the island a unique case study for understanding the dynamic relationship between humans, environment, and climate. These changes are most famously illustrated by the mass extinction of the islandâs megafauna but also include a range of other developments. Given the chronological confluence of human arrival and dramatic transformations of island ecologies, one of the most important overarching questions for research on Madagascar is how best to understand the interconnections between human communities, the environment, and climate. In this review paper, we contribute to the well-established discussion of this complex question by highlighting the potential for new multidisciplinary research collaborations in the southwest part of the island. Specifically, we promote the comparison of paleoclimate indicators from securely dated archaeological and paleontological contexts with Western Indian Ocean climate records, as a productive way to improve the overall resolution of paleoclimate and paleoenvironmental reconstruction for the island. Given new archaeological findings that more than double the length of Madagascarâs human occupation, models of environmental transformation post-human arrival must be reassessed and allow for the possibility of slower and more varied rates of change. Improving the spatial and temporal resolution of paleoclimate reconstruction is critical in distinguishing anthropogenic and climate drivers of environmental change. It will also increase our capacity to leverage archaeological and paleoclimate research toward resolving modern challenges, such as environmental conservation and poverty alleviation