S&P
Fast-DiM: Towards Fast Diffusion Morphs (ieee)
Zander W. Blasingame and Chen Liu
IEEE Security & Privacy, 22(4), pp. 103-114, July-August 2024
DOI: 10.1109/MSEC.2024.3410112
Abstract: Diffusion morphs (DiM) create high-quality face morphs; however, they require a high number of network function evaluations (NFE) to create them. We propose a new DiM pipeline, Fast-DiM, which can create morphs of a similar quality but with fewer NFE.

IJCB 2024
Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs (ieee)
Zander Blasingame and Chen Liu
2024 IEEE International Joint Conference on Biometrics (IJCB 2024), Buffalo, New York, USA, September 15-18, 2024
Abstract: Morphing attacks are an emerging threat to state-of-the-art Face Recognition (FR) systems, which aim to create a single image that contains the biometric information of multiple identities. Diffusion Morphs (DiM) are a recently proposed morphing attack that has achieved state-of-the-art performance for representation-based morphing attacks. However, none of the existing research on DiMs have leveraged the iterative nature of DiMs and left the DiM model as a black box, treating it no differently than one would a Generative Adversarial Network (GAN) or Varational AutoEncoder (VAE). We propose a greedy strategy on the iterative sampling process of DiM models which searches for an optimal step guided by an identity-based heuristic function. We compare our proposed algorithm against ten other state-of-the-art morphing algorithms using the open-source SYN-MAD 2022 competition dataset. We find that our proposed algorithm is unreasonably effective, fooling all of the tested FR systems with an Mated Morph Presentation Match Rate (MMPMR) of 100%, outperforming all other morphing algorithms compared.

IJCB 2024
The Impact of Print-and-Scan in Heterogeneous Morph Evaluation Scenarios (ieee)
Richard E. Neddo, Zander W. Blasingame, and Chen Liu
2024 IEEE International Joint Conference on Biometrics (IJCB 2024), Buffalo, New York, USA, September 15-18, 2024
Abstract: Face morphing attacks pose an increasing threat to face recognition (FR) systems. A morphed photo contains biometric information from two different subjects to take advantage of vulnerabilities in FRs. These systems are particularly susceptible to attacks when the morphs are subjected to print-scanning to mask the artifacts generated during the morphing process. We investigate the impact of print-scanning on morphing attack detection through a series of evaluations on heterogeneous morphing attack scenarios. Our experiments show that we can increase the Mated Morph Presentation Match Rate (MMPMR) by up to 8.48%. Furthermore, when a Single-image Morphing Attack Detection (S-MAD) algorithm is not trained to detect print-scanned morphs the Morphing Attack Classification Error Rate (MACER) can increase by up to 96.12%, indicating significant vulnerability.

TBIOM
Leveraging Diffusion for Strong and High Quality Face Morphing Attacks (ieee)
Zander W. Blasingame and Chen Liu
IEEE Transactions on Biometrics, Behavior, and Identity Science, 6(1), pp. 118-131, January 2024
DOI: 10.1109/TBIOM.2024.3349857
Abstract: Face morphing attacks seek to deceive a Face Recognition (FR) system by presenting a morphed image consisting of the biometric qualities from two different identities with the aim of triggering a false acceptance with one of the two identities, thereby presenting a significant threat to biometric systems. The success of a morphing attack is dependent on the ability of the morphed image to represent the biometric characteristics of both identities that were used to create the image. We present a novel morphing attack that uses a Diffusion-based architecture to improve the visual fidelity of the image and the ability of the morphing attack to represent characteristics from both identities. We demonstrate the effectiveness of the proposed attack by evaluating its visual fidelity via Fréchet Inception Distance (FID). Also, extensive experiments are conducted to measure the vulnerability of FR systems to the proposed attack. The ability of a morphing attack detector to detect the proposed attack is measured and compared against two state-of-the-art GAN-based morphing attacks along with two Landmark-based attacks. Additionally, a novel metric to measure the relative strength between different morphing attacks is introduced and evaluated.

IJCB 2021
Leveraging Adversarial Learning for the Detection of Morphing Attacks (ieee)
Zander Blasingame and Chen Liu
2021 IEEE International Joint Conference on Biometrics (IJCB), August 4-7, 2021, Shenzhen, China
Abstract: An emerging threat towards face recognition systems (FRS) is face morphing attack, which involves the combination of two faces from two different identities into a singular image that would trigger an acceptance for either identity within the FRS. Many of the existing morphing attack detection (MAD) approaches have been trained and evaluated on datasets with limited variation of image characteristics, which can make the approach prone to overfitting. Additionally, there has been difficulty in developing MAD algorithms which can generalize beyond the morphing attack they were trained on, as shown by the most recent NIST FRVT MORPH report. Furthermore, the Single image based MAD (S-MAD) problem has had poor performance, especially when compared to its counterpart, Differential based MAD (D-MAD). In this work, we propose a novel architecture for training deep learning based S-MAD algorithms that leverages adversarial learning to train a more robust detector. The performance of the proposed S-MAD method is benchmarked against the state-of-the-art VGG19 based S-MAD algorithm over 36 experiments using the ISO-IEC 30107-3 evaluation metrics. The proposed method has demonstrated superior and robust detection performance of less than 5% D-EER when evaluated against different morphing attacks.