DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks

Although deep neural networks have been very successful in image-classification tasks, they are prone to adversarial attacks. To generate adversarial inputs, there has emerged a wide variety of techniques, such as black- and whitebox attacks for neural networks. In this paper, we present DeepSearch, a novel fuzzing-based, query-efficient, blackbox attack for image classifiers. Despite its simplicity, DeepSearch is shown to be more effective in finding adversarial inputs than state-of-the-art blackbox approaches. DeepSearch is additionally able to generate the most subtle adversarial inputs in comparison to these approaches

α5β1 Integrin-Mediated Adhesion to Fibronectin Is Required for Axis Elongation and Somitogenesis in Mice

The arginine-glycine-aspartate (RGD) motif in fibronectin (FN) represents the major binding site for α5β1 and αvβ3 integrins. Mice lacking a functional RGD motif in FN (FNRGE/RGE) or α5 integrin develop identical phenotypes characterized by embryonic lethality and a severely shortened posterior trunk with kinked neural tubes. Here we show that the FNRGE/RGE embryos arrest both segmentation and axis elongation. The arrest is evident at about E9.0, corresponding to a stage when gastrulation ceases and the tail bud-derived presomitic mesoderm (PSM) induces α5 integrin expression and assumes axis elongation. At this stage cells of the posterior part of the PSM in wild type embryos are tightly coordinated, express somitic oscillator and cyclic genes required for segmentation, and form a tapered tail bud that extends caudally. In contrast, the posterior PSM cells in FNRGE/RGE embryos lost their tight associations, formed a blunt tail bud unable to extend the body axis, failed to induce the synchronised expression of Notch1 and cyclic genes and cease the formation of new somites. Mechanistically, the interaction of PSM cells with the RGD motif of FN is required for dynamic formation of lamellipodia allowing motility and cell-cell contact formation, as these processes fail when wild type PSM cells are seeded into a FN matrix derived from FNRGE/RGE fibroblasts. Thus, α5β1-mediated adhesion to FN in the PSM regulates the dynamics of membrane protrusions and cell-to-cell communication essential for elongation and segmentation of the body axis

Fibronectin is a stress responsive gene regulated by HSF1 in response to geldanamycin

Fibronectin is an extracellular matrix glycoprotein with key roles in cell adhesion and migration. Hsp90 binds directly to fibronectin and Hsp90 depletion regulates fibronectin matrix stability. Where inhibition of Hsp90 with a C-terminal inhibitor, novobiocin, reduced the fibronectin matrix, treatment with an N-terminal inhibitor, geldanamycin, increased fibronectin levels. Geldanamycin treatment induced a stress response and a strong dose and time dependent increase in fibronectin mRNA via activation of the fibronectin promoter. Three putative heat shock elements (HSEs) were identified in the fibronectin promoter. Loss of two of these HSEs reduced both basal and geldanamycin-induced promoter activity, as did inhibition of the stress-responsive transcription factor HSF1. Binding of HSF1 to one of the putative HSE was confirmed by ChIP under basal conditions, and occupancy shown to increase with geldanamycin treatment. These data support the hypothesis that fibronectin is stress-responsive and a functional HSF1 target gene. COLA42 and LAMB3 mRNA levels were also increased with geldanamycin indicating that regulation of extracellular matrix (ECM) genes by HSF1 may be a wider phenomenon. Taken together, these data have implications for our understanding of ECM dynamics in stress-related diseases in which HSF1 is activated, and where the clinical application of N-terminal Hsp90 inhibitors is intended

Diversity of endophytic fungal communities in twigs and leaves of Quercus suber in healthy and declining cork oak forests

The fungal endophytic communities play an important ecological role in forest ecosystems. Many endophytic fungi are simple commensals, while others establish mutualistic relationships: the plant generally supplies nutrients and shield to the endophyte which, in turn, contributes with secondary metabolites to improve the host's vegetative condition. However, some fungi living in plant tissues may become opportunistic pathogens when their hosts grown under stress. These are particularly relevant in the pathogenesis of serious decline phenomena, which for over twenty years have been causing heavy blight to various forest species, especiallv Quercus spp., in Mediterranean countries. In this study, species diversity, distribution and abundance of endophytic fungi isolated from cork oak leaves and twigs in healthy and declining stands, were compared. The results show a varying assemblage of endophytic species in cork oak trees between healthy and declining stands, characterised by different environmental conditions. Over three years the lowest values of both soil moisture content and De Martonne aridity index were recor- ded in the declining stand. The highest similarities in endophyte canopy assemblages occurred among sites of the same stand. The fungal communities include few dominant species, among which Biscogniauxia mediterranea is the most frequent. Bionectria solani and Trichoderma fertile, endowed with antagonistic activities, were detected only in the healthy stand

Verification and Repair of Neural Networks: A Progress Report on Convolutional Models

Recent public calls for the development of explainable and verifiable AI led to a growing interest in formal verification and repair of machine-learned models. Despite the impressive progress that the learning community has made, models such as deep neural networks remain vulnerable to adversarial attacks, and their sheer size represents a major obstacle to formal analysis and implementation. In this paper we present our current efforts to tackle repair of deep convolutional neural networks using ideas borrowed from Transfer Learning. With results obtained on popular MNIST and CIFAR10 datasets, we show that models of deep convolutional neural networks can be transformed into simpler ones preserving their accuracy, and we discuss how formal repair through convex programming techniques could benefit from this process