Leveraging the power of multimodal large language models (LLMs) offers a promising approach to enhancing the accuracy and interpretability of morphing attack detection (MAD), especially in real-world biometric applications. This work introduces the use of LLMs for differential morphing attack detection (D-MAD). To the best of our knowledge, this is the first study to employ multimodal LLMs to D-MAD using real biometric data. To effectively utilize these models, we design Chain-of-Thought (CoT)-based prompts to reduce failure-to-answer rates and enhance the reasoning behind decisions. Our contributions include: (1) the first application of multimodal LLMs for D-MAD using real data subjects, (2) CoT-based prompt engineering to improve response reliability and explainability, (3) comprehensive qualitative and quantitative benchmarking of LLM performance using data from 54 individuals captured in passport enrollment scenarios, and (4) comparative analysis of two multimodal LLMs: ChatGPT-4o and Gemini providing insights into their morphing attack detection accuracy and decision transparency. Experimental results show that ChatGPT-4o outperforms Gemini in detection accuracy, especially against GAN-based morphs, though both models struggle under challenging conditions. While Gemini offers more consistent explanations, ChatGPT-4o is more resilient but prone to a higher failure-to-answer rate.

Deepfake technology employs sophisticated machine-learning techniques to create highly convincing video and audio recordings of individuals doing or saying things that they never actually did or said. These falsified media pieces have the potential to deceive and manipulate viewers, posing significant risks to their privacy, security, and trust in digital media. In this paper, we present a novel method DualStreamNet for reliable Audio-Video (AV) fake media detection which exploits the complementary information. The proposed DualStreamNet includes independent detector for video and audio modality. We introduced a novel video fake detection framework using a SlowFast encoder as the backbone, and a novel architecture based on a 3D CNN with skip connections. We also introduced novel features to reliably detect audio fakes using a Continuous Wavelet Transform (CWT) Filter Bank that was further processed using the ResNet50 architecture. Finally, the decisions from the video and audio detectors are combined using the logical OR rule to make the final decision. Extensive experiments were performed on two publicly available audio-video fake datasets: FakeAVCeleb and SWAN-DF. The obtained results indicate the improved detection accuracy of the proposed method compared to existing methods.

Morphing attack detection is a critical component of face recognition systems, ensuring reliable applications in border control. In this work, we propose a novel Differential Morphing Attack Detection (D-MAD) method capable of effectively capturing variations in differential features extracted using a Face Recognition System (FRS). Our proposed model, TimFusNet, combines time-frequency and temporal variations from the difference in facial embeddings computed from passport and trusted face images. The TimFusNet architecture comprises two branches: the first branch captures time-frequency variations from the input differential embeddings, while the second branch focuses on extracting temporal variations. The features from both branches are concatenated and passed through a self-attention layer to reliably detect morphing attacks on FRS. We conducted extensive experiments using a morphing dataset constructed from the FRGC dataset, employing five different morphing generation techniques. The detection performance of TimFusNet was benchmarked against four existing D-MAD techniques under two evaluation protocols. The results demonstrate that TimFusNet delivers outstanding detection performance across both protocols.

Morphing attacks can successfully deceive face recognition systems, resulting in unreliable access control, especially in the border control scenario. Consequently, the development of Morphing Attack Detection (MAD) algorithms is crucial for detecting morphing attacks based on either a single facial image (S-MAD) or two facial images (Differential-MAD or D-MAD). In this work, we proposed a novel D-MAD approach, PoolAtnRes, to reliably detect morphing attacks. The proposed PoolAtnRes architecture is constructed using three main functional blocks, namely convolution pooling, Hybrid Attention and Residual blocks, which are serially connected to detect morphing attacks. Extensive experiments were performed on the newly constructed morphing dataset using nine morphing-generation techniques. The detection performance of the proposed PoolAtnRes model was compared with three state-of-the-art (SOTA) D-MAD techniques with different performance evaluation protocols to benchmark its generalizabllity to unseen morphing generation. The results obtained indicated the best performance of the proposed PoolAtnRes D-MAD.

Facial biometrics are an essential components of smartphones to ensure reliable and trustworthy authentication. However, face biometric systems are vulnerable to Presentation Attacks (PAs), and the availability of more sophisticated presentation attack instruments such as 3D silicone face masks will allow attackers to deceive face recognition systems easily. In this work, we propose a novel Presentation Attack Detection (PAD) algorithm based on 3D point clouds captured using the frontal camera of a smartphone to detect presentation attacks. The proposed PAD algorithm, VoxAtnNet, processes 3D point clouds to obtain voxelization to preserve the spatial structure. Then, the voxelized 3D samples were trained using the novel convolutional attention network to detect PAs on the smartphone. Extensive experiments were carried out on the newly constructed 3D face point cloud dataset comprising bona fide and two different 3D PAIs (3D silicone face mask and wrap photo mask), resulting in 3480 samples. The performance of the proposed method was compared with existing methods to benchmark the detection performance using three different evaluation protocols. The experimental results demonstrate the improved performance of the proposed method in detecting both known and unknown face presentation attacks.

Biometric verification systems are deployed in various security-based access-control applications that require user-friendly and reliable person verification. Among the different biometric characteristics, fingervein biometrics have been extensively studied owing to their reliable verification performance. Furthermore, fingervein patterns reside inside the skin and are not visible outside; therefore, they possess inherent resistance to presentation attacks and degradation due to external factors. In this paper, we introduce a novel fingervein verification technique using a convolutional multihead attention network called VeinAtnNet. The proposed VeinAtnNet is designed to achieve light weight with a smaller number of learnable parameters while extracting discriminant information from both normal and enhanced fingervein images. The proposed VeinAtnNet was trained on the newly constructed fingervein dataset with 300 unique fingervein patterns that were captured in multiple sessions to obtain 92 samples per unique fingervein. Extensive experiments were performed on the newly collected dataset FV-300 and the publicly available FV-USM and FV-PolyU fingervein dataset. The performance of the proposed method was compared with five state-of-the-art fingervein verification systems, indicating the efficacy of the proposed VeinAtnNet.

Face morphing attack detection is emerging as an increasingly challenging problem owing to advancements in high-quality and realistic morphing attack generation. Reliable detection of morphing attacks is essential because these attacks are targeted for border control applications. This paper presents a multispectral framework for differential morphing-attack detection (D-MAD). The D-MAD methods are based on using two facial images that are captured from the ePassport (also called the reference image) and the trusted device (for example, Automatic Border Control (ABC) gates) to detect whether the face image presented in ePassport is morphed. The proposed multi-spectral D-MAD framework introduce a multispectral image captured as a trusted capture to acquire seven different spectral bands to detect morphing attacks. Extensive experiments were conducted on the newly created Multispectral Morphed Datasets (MSMD) with 143 unique data subjects that were captured using both visible and multispectral cameras in multiple sessions. The results indicate the superior performance of the proposed multispectral framework compared to visible images.

The face morphing process will combine two or more facial images to generate a single morphed facial image demonstrating Face Recognition Systems (FRS) vulnerability. The attack potential of the morphing image directly depends on the perceptual image quality, and when generated with no visible artefacts, it can deceive both human observers and automatic FRS. The current softwares for face morphing generates a morphing image with ghosting artefacts, especially in the eye region, nose and mouth area, which may serve as a potential cue to detect morphing attacks. Hence in this work, we introduce a new dataset comprising 10710 facial images before and after manual post-processing to reduce the visual artefacts and to generate high-quality attacks. Further, we propose a novel single image-based Morph Attack Detection (S-MAD) technique based on the ensemble of features and classifiers using the scale-space domain. The novel concept in the proposed method is the multilevel fusion that combines the comparison scores from different features and classifiers. Extensive experiments are carried out on the newly generated high-quality face images with (i) Morphs before post-processing and (ii) Morphs after post-processing. Further, the experiments are also carried out on two different mediums such as (i) Digital and (ii) Print-scan (or re-digitized) with and without compression. Extensive experimental results are performed to benchmark the detection performance with the existing S-MAD techniques. Obtained results indicate the best performance of the proposed method over existing methods.

Facial biometrics are widely deployed in smartphone-based applications because of their usability and increased verification accuracy in unconstrained scenarios. The evolving applications of smartphone-based facial recognition have also increased Presentation Attacks (PAs), where an attacker can present a Presentation Attack Instrument (PAI) to maliciously gain access to the application. Because the materials used to generate PAI are not deterministic, the detection of unknown presentation attacks is challenging. In this paper, we present an acoustic echo-based face Presentation Attack Detection (PAD) on a smartphone in which the PAs are detected based on the reflection profiles of the transmitted signal. We propose a novel transmission signal based on the wide pulse that allows us to model the background noise before transmitting the signal and increase the Signal-to-Noise Ratio (SNR). The received signal reflections were processed to remove background noise and accurately represent reflection characteristics. The reflection profiles of the bona fide and PAs are different owing to the different reflection characteristics of the human skin and artefact materials. Extensive experiments are presented using the newly collected Acoustic Sound Echo Dataset (ASED) with 4807 samples captured from bona fide and four different types of PAIs, including print (two types), display, and silicone face-mask attacks. The obtained results indicate the robustness of the proposed method for detecting unknown face presentation attacks.

Face recognition systems (FRS) are vulnerable to different kinds of attacks. Morphing attack combines multiple face images to obtain a single face image that can verify equally against all contributing subjects. Various Morphing Attack Detection (MAD) algorithms have been proposed in recent years albeit limited generalizability. We present a new approach for MAD in this work with better generalization than state-of-the-art (SOTA) algorithms. We propose an end-to-end multi-stage encoder-decoder network for learning the residuals of morphing process to detect attacks. Leveraging the residuals, we learn an efficient classifier using cross-entropy loss and asymmetric loss. The use of asymmetric loss in our approach is motivated by imbalanced distribution of morphs and bona fides. An extensive set of experiments are conducted on five different datasets consisting of two landmark based and three Generative Adversarial Network (GAN) based morphs in various settings such as digital, print-scan and print-scan-compression. We first demonstrate a near-ideal performance of the proposed MAD with Detection Equal Error Rate (D-EER) of 0% in the best case and 2.58% in the worst case in the digital domain in closed-set protocol, i.e., known attacks. Further, we demonstrate the applicability of the proposed approach on 60 different combinations where the testing set contains unknown morphing attacks in open-set protocol to illustrate the generalization ability of our proposed approach. Through training the proposed approach on landmark-based morph generation data alone, we obtain an EER of 3.59% in the best case and 12.89% in the worst case for morphed images in the digital domain, reducing the error rates from 45.67% and 30.23% respectively, in open-set protocol. We further present an extensive analysis of the proposed approach through Class Activation Maps (CAM) to explain the decisions using by making use of three complementary CAM analysis.

Face morphing attacks are evolving as a significant threat to the Face Recognition Systems (FRS) operating in border control and passport issuance. As newborn face has very limited discriminative facial characteristics, it is challenging for both human and machines to verify the newborns based on the facial biometrics accurately. Further, the introduction of face morphing elevates the problem of baby trafficking as it can challenge both human and machine-based facial verification. In this paper, we pose a question if the morphed images of newborns can threaten FRS and present first systematic study on the vulnerability analysis of FRS towards morphed faces of newborns. To effectively benchmark threat of newborns’ facial morphing attacks, we introduce a new face morphing dataset constructed based on 42 unique newborns with 852 bona fide and 2451 morphing images. Extensive experiments are carried out on the newly constructed dataset to benchmark the vulnerability against both Commercial-Off-The-Shelf (COTS) FRS (Cognitec FaceVACS-SDK Version 9.4.2) and deep learning based FRS (Arcface) for three different morphing factors. Further, we also evaluate the performance of Morphing Attack Detection (MAD) in detecting such morphing attacks of newborn faces. We conduct experiments on four different Off-The-Shelf MAD techniques to benchmark the detection performance on newborn morph attacks.

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.

Face morphing attacks have demonstrated a severe threat in the passport issuance protocol that weakens the border control operations. A morphed face images if used after printing and scanning (re-digitizing) to obtain a passport is very challenging to be detected as attack. In this paper, we present a novel method to detect such morphing attacks using an ensemble of features computed on the scale-space representation derived from the color space for a given image. Given the limited availability of datasets representing realistic morphing attacks, we introduce and present a new print-scan image dataset of morphed face images. Experiments are carried out on the two different datasets and compared with sixteen existing state-of-art Morphing Attack Detection (MAD) mechanism based on single image MAD (S-MAD). The proposed approach indicates a superior MAD performance on both datasets suggesting the applicability in operational scenarios.

Face morphing attacks have raised critical concerns as they demonstrate a new vulnerability of Face Recognition Systems (FRS), which are widely deployed in border control applications. The face morphing process uses the images from multiple data subjects and performs an image blending operation to generate a morphed image of high quality. The generated morphed image exhibits similar visual characteristics corresponding to the biometric characteristics of the data subjects that contributed to the composite image and thus making it difficult for both humans and FRS, to detect such attacks. In this paper, we report a systematic investigation on the vulnerability of the Commercial-Off- The-Shelf (COTS) FRS when morphed images under the influence of ageing are presented. To this extent, we have introduced a new morphed face dataset with ageing derived from the publicly available MORPH II face dataset, which we refer to as MorphAge dataset. The dataset has two bins based on age intervals, the first bin - MorphAge-I dataset has 1002 unique data subjects with the age variation of 1 year to 2 years while the MorphAge-II dataset consists of 516 data subjects whose age intervals are from 2 years to 5 years. To effectively evaluate the vulnerability for morphing attacks, we also introduce a new evaluation metric, namely the Fully Mated Morphed Presentation Match Rate (FMMPMR), to quantify the vulnerability effectively in a realistic scenario. Extensive experiments are carried out using two different COTS FRS (COTS I Cognitec FaceVACS-SDK Version 9.4.2 and COTS II - Neurotechnology version 10.0) to quantify the vulnerability with ageing. Further, we also evaluate five different Morph Attack Detection (MAD) techniques to benchmark their detection performance with respect to ageing.

Wrist-wearable devices such as smartwatch hardware have gained popularity as they provide quick access to various information and easy access to multiple applications. Among the numerous smartwatch applications, user verification based on the handwriting is gaining momentum by considering its reliability and user-friendliness. In this paper, we present a novel technique for user verification using a smartwatch based writing pattern or style. The proposed approach leverages accelerometer data captured from the smartwatch that is further represented using 2D Continuous Wavelet Transform (CWT) and deep features extracted using the pre-trained ResNet50. These features are classified using an ensemble of classifiers to make the final decision on user verification. Extensive experiments are carried out on a newly captured dataset using two different smartwatches with three different writing scenarios (or activities). Experimental results provide critical insights and analysis of the results in such a verification scenario.

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 demising 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.

Face recognition systems are widely known for their vulnerability against presentation attacks or spoofing attacks. The exponential deployment of face recognition systems has been further challenged even by the simple and low-cost face artefacts generated using conventional printers. In this paper, we present a novel scheme to detect face presentation attacks posed by high-quality print attacks which are relatively difficult to detect. The proposed scheme leverages the phase information extracted from the spatial-frequency representation of the given image. We also present a new face presentation attack database collected using the iPhone 6S. The new database is comprised of 100 subjects collected in two different sessions that have resulted in a total of 31228 samples (or images). Extensive experiments are carried out on the newly constructed database and the obtained results show the improved performance of the proposed scheme when compared aaainst six different state-of-the-art methods.

Multispectral face recognition systems are widely used in various access control applications. The vulnerability of multispectral face recognition sensors towards low-cost Presentation Attack Instrument (PAI) such as printed photos used in attacks has emerged as a serious security threat. In this paper, we present a novel framework to detect presentation attacks against an extended multispectral face sensor. The proposed framework stems from the idea of exploring the complementary information available from different bands of an extended multispectral face sensor. To this extent, two different frameworks are proposed where the first framework is based on image fusion and the second builds on the Presentation Attack Detection (PAD) score level fusion. Extensive experiments are carried out on the extended multispectral face sensor database comprising of 50 subjects with two different presentation attacks generated using the printed photo artefacts. The obtained results indicate the superior performance of the PAD score level fusion on detecting both known and unknown attacks.

The Face Recognition System (FRS) is known to be vulnerable to the attacks using the morphed face. As the use of face characteristics are mandatory in the electronic passport (ePass), morphing attacks have raised the potential concerns in the border security. In this paper, we analyze the vulnerability of the FRS to the new attack performed using the averaged face. The averaged face is generated by simple pixel level averaging of two face images corresponding to two different subjects. We benchmark the vulnerability of the commercial FRS to both conventional morphing and averaging based face attacks. We further propose a novel algorithm based on the collaborative representation of the micro-texture features that are extracted from the colour space to reliably detect both morphed and averaged face attacks on the FRS. Extensive experiments are carried out on the newly constructed morphed and averaged face image database with 163 subjects. The database is built by considering the real-life scenario of the passport issuance that typically accepts the printed passport photo from the applicant that is further scanned and stored in the ePass. Thus, the newly constructed database is built to have the print-scanned bonafide, morphed and averaged face samples. The obtained results have demonstrated the improved performance of the proposed scheme on print-scanned morphed and averaged face database.