Generating 2D and 3D Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution

A master face is a face image that passes face-based identity authentication for a high percentage of the population. These faces can be used to impersonate, with a high probability of success, any user, without having access to any user information. We optimize these faces for 2D and 3D face verification models, by using an evolutionary algorithm in the latent embedding space of the StyleGAN face generator. For 2D face verification, multiple evolutionary strategies are compared, and we propose a novel approach that employs a neural network to direct the search toward promising samples, without adding fitness evaluations. The results we present demonstrate that it is possible to obtain a considerable coverage of the identities in the LFW or RFW datasets with less than 10 master faces, for six leading deep face recognition systems. In 3D, we generate faces using the 2D StyleGAN2 generator and predict a 3D structure using a deep 3D face reconstruction network. When employing two different 3D face recognition systems, we are able to obtain a coverage of 40%-50%. Additionally, we present the generation of paired 2D RGB and 3D master faces, which simultaneously match 2D and 3D models with high impersonation rates.Comment: accepted for publication in IEEE Transactions on Biometrics, Behavior, and Identity Science (TBIOM). This paper extends arXiv:2108.01077 that was accepted to IEEE FG 202

Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint

Many practical settings call for the reconstruction of temporal signals from corrupted or missing data. Classic examples include decoding, tracking, signal enhancement and denoising. Since the reconstructed signals are ultimately viewed by humans, it is desirable to achieve reconstructions that are pleasing to human perception. Mathematically, perfect perceptual-quality is achieved when the distribution of restored signals is the same as that of natural signals, a requirement which has been heavily researched in static estimation settings (i.e. when a whole signal is processed at once). Here, we study the problem of optimal causal filtering under a perfect perceptual-quality constraint, which is a task of fundamentally different nature. Specifically, we analyze a Gaussian Markov signal observed through a linear noisy transformation. In the absence of perceptual constraints, the Kalman filter is known to be optimal in the MSE sense for this setting. Here, we show that adding the perfect perceptual quality constraint (i.e. the requirement of temporal consistency), introduces a fundamental dilemma whereby the filter may have to "knowingly" ignore new information revealed by the observations in order to conform to its past decisions. This often comes at the cost of a significant increase in the MSE (beyond that encountered in static settings). Our analysis goes beyond the classic innovation process of the Kalman filter, and introduces the novel concept of an unutilized information process. Using this tool, we present a recursive formula for perceptual filters, and demonstrate the qualitative effects of perfect perceptual-quality estimation on a video reconstruction problem

The Effect of Microstructural Imperfections on Corrosion Fatigue of Additively Manufactured ER70S-6 Alloy Produced by Wire Arc Deposition

This study aims at evaluating the effect of microstructure imperfections on the corrosion fatigue performance of an ER70S-6 alloy produced by wire arc additive manufacturing (WAAM) process, in a 3.5% NaCl solution. For reference, a regular ST-37 alloy with relatively similar chemical composition was considered as a counterpart alloy. This was justified by the fact that the ER70S-6 alloy is usually used for conventional welding of ST-37 steel. The results obtained indicated that while the ST-37 alloy exhibited fatigue strength of 240 MPa in the corrosive solution, the additively manufactured ER70S-6 alloy showed fatigue strength of only 140 MPa. These differences were related to microstructural imperfections that are inherently produced during the WAAM process.</jats:p

Stress Corrosion Analysis and Direct Cell Viability of Biodegradable Zn-Fe-Ca Alloy in In-Vitro Conditions

Due to the excellent biocompatibility of Zn and Zn-based alloys, researchers have shown great interest in developing biodegradable implants based on zinc. Furthermore, zinc is an essential component of many enzymes and proteins. The human body requires ~15 mg of Zn per day, and there is minimal concern for systemic toxicity from a small zinc-based cardiovascular implant, such as an arterial stent. However, biodegradable Zn-based implants have been shown to provoke local fibrous encapsulation reactions that may isolate the implant from its surrounding environment and interfere with implant function. The development of biodegradable implants made from Zn-Fe-Ca alloy was designed to overcome the problem of fibrous encapsulation. In a previous study made by the authors, the Zn-Fe-Ca system demonstrated a suitable corrosion rate that was higher than that of pure Zn and Zn-Fe alloy. The Zn-Fe-Ca system also showed adequate mechanical properties and a unique microstructure that contained a secondary Ca-reach phase. This has raised the promise that the tested alloy could serve as a biodegradable implant metal. The present study was conducted to further evaluate this promising Zn alloy. Here, we assessed the material’s corrosion performance in terms of cyclic potentiodynamic polarization analysis and stress corrosion behavior in terms of slow strain rate testing (SSRT). We also assessed the ability of cells to survive on the alloy surface by direct cell culture test. The results indicate that the alloy develops pitting corrosion, but not stress corrosion under phosphate-buffered saline (PBS) and air environment. The direct cell viability test demonstrates the successful adherence and growth of cells on the alloy surface

From E-MAPs to module maps: dissecting quantitative genetic interactions using physical interactions

Recent technological breakthroughs allow the quantification of hundreds of thousands of genetic interactions (GIs) in Saccharomyces cerevisiae. The interpretation of these data is often difficult, but it can be improved by the joint analysis of GIs along with complementary data types. Here, we describe a novel methodology that integrates genetic and physical interaction data. We use our method to identify a collection of functional modules related to chromosomal biology and to investigate the relations among them. We show how the resulting map of modules provides clues for the elucidation of function both at the level of individual genes and at the level of functional modules

Protein Dynamics in Individual Human Cells: Experiment and Theory

A current challenge in biology is to understand the dynamics of protein circuits in living human cells. Can one define and test equations for the dynamics and variability of a protein over time? Here, we address this experimentally and theoretically, by means of accurate time-resolved measurements of endogenously tagged proteins in individual human cells. As a model system, we choose three stable proteins displaying cell-cycle–dependant dynamics. We find that protein accumulation with time per cell is quadratic for proteins with long mRNA life times and approximately linear for a protein with short mRNA lifetime. Both behaviors correspond to a classical model of transcription and translation. A stochastic model, in which genes slowly switch between ON and OFF states, captures measured cell–cell variability. The data suggests, in accordance with the model, that switching to the gene ON state is exponentially distributed and that the cell–cell distribution of protein levels can be approximated by a Gamma distribution throughout the cell cycle. These results suggest that relatively simple models may describe protein dynamics in individual human cells