MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders

Investigating new methods of creating face morphing attacks is essential to foresee novel attacks and help mitigate them. Creating morphing attacks is commonly either performed on the image-level or on the representation-level. The representation-level morphing has been performed so far based on generative adversarial networks (GAN) where the encoded images are interpolated in the latent space to produce a morphed image based on the interpolated vector. Such a process was constrained by the limited reconstruction fidelity of GAN architectures. Recent advances in the diffusion autoencoder models have overcome the GAN limitations, leading to high reconstruction fidelity. This theoretically makes them a perfect candidate to perform representation-level face morphing. This work investigates using diffusion autoencoders to create face morphing attacks by comparing them to a wide range of image-level and representation-level morphs. Our vulnerability analyses on four state-of-the-art face recognition models have shown that such models are highly vulnerable to the created attacks, the MorDIFF, especially when compared to existing representation-level morphs. Detailed detectability analyses are also performed on the MorDIFF, showing that they are as challenging to detect as other morphing attacks created on the image- or representation-level. Data and morphing script are made public: https://github.com/naserdamer/MorDIFF.Comment: Accepted at the 11th International Workshop on Biometrics and Forensics 2023 (IWBF 2023

SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data

Recent deep face recognition models proposed in the literature utilized large-scale public datasets such as MS-Celeb-1M and VGGFace2 for training very deep neural networks, achieving state-of-the-art performance on mainstream benchmarks. Recently, many of these datasets, e.g., MS-Celeb-1M and VGGFace2, are retracted due to credible privacy and ethical concerns. This motivates this work to propose and investigate the feasibility of using a privacy-friendly synthetically generated face dataset to train face recognition models. Towards this end, we utilize a class-conditional generative adversarial network to generate class-labeled synthetic face images, namely SFace. To address the privacy aspect of using such data to train a face recognition model, we provide extensive evaluation experiments on the identity relation between the synthetic dataset and the original authentic dataset used to train the generative model. Our reported evaluation proved that associating an identity of the authentic dataset to one with the same class label in the synthetic dataset is hardly possible. We also propose to train face recognition on our privacy-friendly dataset, SFace, using three different learning strategies, multi-class classification, label-free knowledge transfer, and combined learning of multi-class classification and knowledge transfer. The reported evaluation results on five authentic face benchmarks demonstrated that the privacy-friendly synthetic dataset has a high potential to be used for training face recognition models, achieving, for example, a verification accuracy of 91.87% on LFW using multi-class classification and 99.13% using the combined learning strategy. The training code and the synthetic face image dataset are publicly released 1 1 https://github.com/fdbtrs/SFace-Privacy-friendly-and-Accurate-Face-Recognition-using-Synthetic-Data

PocketNet: Extreme Lightweight Face Recognition Network Using Neural Architecture Search and Multistep Knowledge Distillation

Deep neural networks have rapidly become the mainstream method for face recognition (FR). However, this limits the deployment of such models that contain an extremely large number of parameters to embedded and low-end devices. In this work, we present an extremely lightweight and accurate FR solution, namely PocketNet. We utilize neural architecture search to develop a new family of lightweight face-specific architectures. We additionally propose a novel training paradigm based on knowledge distillation (KD), the multi-step KD, where the knowledge is distilled from the teacher model to the student model at different stages of the training maturity. We conduct a detailed ablation study proving both, the sanity of using NAS for the specific task of FR rather than general object classification, and the benefits of our proposed multi-step KD. We present an extensive experimental evaluation and comparisons with the state-of-the-art (SOTA) compact FR models on nine different benchmarks including large-scale evaluation benchmarks such as IJB-C and MegaFace. PocketNets have consistently advanced the SOTA FR performance on nine mainstream benchmarks when considering the same level of model compactness. With 0.92M parameters, our smallest network PocketNetS-128 achieved very competitive results to recent SOTA compacted models that contain up to 4M parameters. Training codes and pre-trained models are public

A DETAILED STUDY OF STRANGE PARTICLE PRODUCTION IN e+ e- ANNIHILATION AT HIGH-ENERGY

Results onK0 and Λ production ine+e− annihilation at c.m. energies of 14, 22 and 34 GeV are presented. The shape of theK0 and Λ differential cross sections are very similar to each other and to those of π±,K± and$p(\bar p)$. Scaling violations are observed forK0 production. We obtain a value for the probability to produce strange quark-antiquark pairs relative to that to produce up or down quark-antiquark pairs of 0.35±0.02±0.05. The value ofRh=σ(e+e-→hX)/σµµ is shown to rise steadily with c.m. energy for all particle species. At 34 GeV we find 1.48±0.05K0 and 0.31±0.03 Λ per event. We have searched for possible Λ polarization. The production ofK0's and Λ's in jets is examined as a function ofpT2 and rapidity and compared to that of all charged particles; the yields in two and three jets are also investigated. Results are presented from events with two baryons$(\Lambda ,\bar \Lambda ,por\bar p)$ observed

Brain resuscitation in the drowning victim

Item does not contain fulltextDrowning is a leading cause of accidental death. Survivors may sustain severe neurologic morbidity. There is negligible research specific to brain injury in drowning making current clinical management non-specific to this disorder. This review represents an evidence-based consensus effort to provide recommendations for management and investigation of the drowning victim. Epidemiology, brain-oriented prehospital and intensive care, therapeutic hypothermia, neuroimaging/monitoring, biomarkers, and neuroresuscitative pharmacology are addressed. When cardiac arrest is present, chest compressions with rescue breathing are recommended due to the asphyxial insult. In the comatose patient with restoration of spontaneous circulation, hypoxemia and hyperoxemia should be avoided, hyperthermia treated, and induced hypothermia (32-34 degrees C) considered. Arterial hypotension/hypertension should be recognized and treated. Prevent hypoglycemia and treat hyperglycemia. Treat clinical seizures and consider treating non-convulsive status epilepticus. Serial neurologic examinations should be provided. Brain imaging and serial biomarker measurement may aid prognostication. Continuous electroencephalography and N20 somatosensory evoked potential monitoring may be considered. Serial biomarker measurement (e.g., neuron specific enolase) may aid prognostication. There is insufficient evidence to recommend use of any specific brain-oriented neuroresuscitative pharmacologic therapy other than that required to restore and maintain normal physiology. Following initial stabilization, victims should be transferred to centers with expertise in age-specific post-resuscitation neurocritical care. Care should be documented, reviewed, and quality improvement assessment performed. Preclinical research should focus on models of asphyxial cardiac arrest. Clinical research should focus on improved cardiopulmonary resuscitation, re-oxygenation/reperfusion strategies, therapeutic hypothermia, neuroprotection, neurorehabilitation, and consideration of drowning in advances made in treatment of other central nervous system disorders