An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences

Together with impressive advances touching every aspect of our society, AI technology based on Deep Neural Networks (DNN) is bringing increasing security concerns. While attacks operating at test time have monopolised the initial attention of researchers, backdoor attacks, exploiting the possibility of corrupting DNN models by interfering with the training process, represent a further serious threat undermining the dependability of AI techniques. In backdoor attacks, the attacker corrupts the training data to induce an erroneous behaviour at test time. Test-time errors, however, are activated only in the presence of a triggering event. In this way, the corrupted network continues to work as expected for regular inputs, and the malicious behaviour occurs only when the attacker decides to activate the backdoor hidden within the network. Recently, backdoor attacks have been an intense research domain focusing on both the development of new classes of attacks, and the proposal of possible countermeasures. The goal of this overview is to review the works published until now, classifying the different types of attacks and defences proposed so far. The classification guiding the analysis is based on the amount of control that the attacker has on the training process, and the capability of the defender to verify the integrity of the data used for training, and to monitor the operations of the DNN at training and test time. Hence, the proposed analysis is suited to highlight the strengths and weaknesses of both attacks and defences with reference to the application scenarios they are operating in

An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences

Together with impressive advances touching every aspect of our society, AI technology based on Deep Neural Networks (DNN) is bringing increasing security concerns. While attacks operating at test time have monopolised the initial attention of researchers, backdoor attacks, exploiting the possibility of corrupting DNN models by interfering with the training process, represent a further serious threat undermining the dependability of AI techniques. In backdoor attacks, the attacker corrupts the training data to induce an erroneous behaviour at test time. Test-time errors, however, are activated only in the presence of a triggering event. In this way, the corrupted network continues to work as expected for regular inputs, and the malicious behaviour occurs only when the attacker decides to activate the backdoor hidden within the network. Recently, backdoor attacks have been an intense research domain focusing on both the development of new classes of attacks, and the proposal of possible countermeasures. The goal of this overview is to review the works published until now, classifying the different types of attacks and defences proposed so far. The classification guiding the analysis is based on the amount of control that the attacker has on the training process, and the capability of the defender to verify the integrity of the data used for training, and to monitor the operations of the DNN at training and test time. Hence, the proposed analysis is suited to highlight the strengths and weaknesses of both attacks and defences with reference to the application scenarios they are operating in

Manipulation and generation of synthetic satellite images using deep learning models

Generation and manipulation of digital images based on deep learning (DL) are receiving increasing attention for both benign and malevolent uses. As the importance of satellite imagery is increasing, DL has started being used also for the generation of synthetic satellite images. However, the direct use of techniques developed for computer vision applications is not possible, due to the different nature of satellite images. The goal of our work is to describe a number of methods to generate manipulated and synthetic satellite images. To be specific, we focus on two different types of manipulations: full image modification and local splicing. In the former case, we rely on generative adversarial networks commonly used for style transfer applications, adapting them to implement two different kinds of transfer: (i) land cover transfer, aiming at modifying the image content from vegetation to barren and vice versa and (ii) season transfer, aiming at modifying the image content from winter to summer and vice versa. With regard to local splicing, we present two different architectures. The first one uses image generative pretrained transformer and is trained on pixel sequences in order to predict pixels in semantically consistent regions identified using watershed segmentation. The second technique uses a vision transformer operating on image patches rather than on a pixel by pixel basis. We use the trained vision transformer to generate synthetic image segments and splice them into a selected region of the to-be-manipulated image. All the proposed methods generate highly realistic, synthetic, and satellite images. Among the possible applications of the proposed techniques, we mention the generation of proper datasets for the evaluation and training of tools for the analysis of satellite images. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI

Detecting Deepfake Videos in Data Scarcity Conditions by Means of Video Coding Features

The most powerful deepfake detection methods developed so far are based on deep learning, requiring that large amounts of training data representative of the specific task are available to the trainer. In this paper, we propose a feature-based method for video deepfake detection that can work in data scarcity conditions, that is, when only very few examples are available to the forensic analyst. The proposed method is based on video coding analysis and relies on a simple footprint obtained from the motion prediction modes in the video sequence. The footprint is extracted from video sequences and used to train a simple linear Support Vector Machine classifier. The effectiveness of the proposed method is validated experimentally on three different datasets, namely, a synthetic street video dataset and two datasets of Deepfake face videos

Ethnic fragmentation and degree of urbanization strongly affect the discrimination power of Y-STR haplotypes in central Sahel

Y chromosome short tandem repeats (Y-STRs) are commonly used to identify male lineages for investigative and judicial purposes and could represent the only source of male-specific genetic information from unbalanced female-male mixtures. The Yfiler Plus multiplex, which includes twenty conventional and seven rapidly-mutating Y-STRs, represents the most discriminating patrilineal system commercially available to date. Over the past five years, this multiplex has been used to analyze several Eurasian populations, with a reported discrimination capacity (DC) approaching or corresponding to the highest possible value. However, despite the inclusion of rapidly mutating Y-STRs, extensive haplotype sharing was still reported for some African populations due to a number of different factors affecting the effective population size. In the present study, we analyzed 27 Y-STRs included in the Yfiler Plus multiplex and 82 Y-SNPs in central Sahel (northern Cameroon and western Chad), an African region characterized by a strong ethnic fragmentation and linguistic diversity. We evaluated the effects of population sub-structuring on genetic diversity by stratifying a sample composed of 431 males according to their ethnicity (44 different ethnic groups) and urbanization degree (four villages and four towns). Overall, we observed a low discrimination capacity (DC = 0.90), with 71 subjects (16.5 %) sharing 27 Y-STR haplotypes. Haplotype sharing was essentially limited to subjects with the same binary haplogroup, coming from the same location and belonging to the same ethnic group. Haplotype sharing was much higher in rural areas (average DC = 0.83) than urban settlements (average DC = 0.96) with a significant correlation between DC and census size (r = 0.89; p = 0.003). Notably, we found that genetic differentiation between villages from the same country (ΦST = 0.14) largely exceeded that found among countries (ΦST = 0.02). These findings have important implications for the choice of the appropriate reference population database to evaluate the statistical relevance of forensic Y-haplotype matches

Technological development of hydroalcoholic extractive solutions from Calendula officinalis L. flowers by using factorial design

L., pelo processo de maceração (MAC) e de turbo-extração (TURB), empregando planejamento fatorial 32 (dois fatores e três níveis), para avaliar a influência do tempo de extração (MAC = 5, 10 e 15 dias; TURB = 5, 15 e 25 min) e da relação planta:solvente (2,5, 7,5 e 12,5%, m/V) sobre o teor de flavonóides totais e resíduo seco nas soluções extrativas. Para o processo de maceração, a relação planta: solvente de 12,5% e o tempo de 15 dias resultaram nas melhores condições para alcançar um maior teor de flavonóides totais, em relação à turbo-extração que obteve o mesmo desempenho em 5 min. Para ambos os processos, o resíduo seco foi proporcional à relação planta:solvente.The work aimed at the development of extractive solutions from the flowers of Calendula officinalis, through maceration (MAC) and turbo-extraction (TURB) processes, employing the 32 (two factors and three levels) factorial design, in order to evaluate the influence of the time (MAC = 5, 10 and 15 days; TURB = 5, 15 and 25 min) and of the plant: solvent ratio (2.5, 7.5 and 12.5%, w/v) on the flavonoid content and the dry residue yields in the extractive solutions. For the maceration process, the 12.5% (w/v) plant: solvent ratio over 15 days were the best conditions to achieve the highest flavonoid content, in the turbo-extraction process the same performance was achieved in 5 min. For both processes the dry residue was proportional to the plant: solvent ratio.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Expression and Function of Gonadotropin-releasing Hormone (GnRH) Receptor in Human Olfactory GnRH-secreting Neurons AN AUTOCRINE GnRH LOOP UNDERLIES NEURONAL MIGRATION

Olfactory neurons and gonadotropin-releasing hormone (GnRH) neurons share a common origin during organogenesis. Kallmann's syndrome, clinically characterized by anosmia and hypogonadotropic hypogonadism, is due to an abnormality in the migration of olfactory and GnRH neurons. We recently characterized the human FNC-B4 cell line, which retains properties present in vivo in both olfactory and GnRH neurons. In this study, we found that FNC-B4 neurons expressed GnRH receptor and responded to GnRH with time- and dose-dependent increases in GnRH gene expression and protein release (up to 5-fold). In addition, GnRH and its analogs stimulated cAMP production and calcium mobilization, although at different biological thresholds (nanomolar for cAMP and micromolar concentrations for calcium). We also observed that GnRH triggered axon growth, actin cytoskeleton remodeling, and a dose-dependent increase in migration (up to 3-4-fold), whereas it down-regulated nestin expression. All these effects were blocked by a specific GnRH receptor antagonist, cetrorelix. We suggest that GnRH, secreted by olfactory neuroblasts, acts in an autocrine pattern to promote differentiation and migration of those cells that diverge from the olfactory sensory lineage and are committed to becoming GnRH neurons

Transverse Λ0\Lambda^0 polarization in inclusive quasi-real photoproduction at the current fragmentation

It is shown that the recent HERMES data on the transverse $\Lambda^0$ polarization in the inclusive quasi-real photoproduction at $x_F>0$ can be accommodated by the strange quark scattering model. Relations with the quark recombination approach are discussed.Comment: 5 pages, 3 figures, accepted by Eur. Phys. J.

Lambda polarization and single-spin left-right asymmetry in diffractive hadron-hadron collisions

We discuss Lambda polarization and single-spin left-right asymmetry in diffractive hadron-hadron scattering at high energies. We show that the physical picture proposed in a recent Letter is consistent with the experimental observation that $\Lambda$ polarization in the diffractive process, $pp\to \Lambda K^+p$, is much higher than that in the inclusive reaction, $pp\to \Lambda X$. We make predictions for the left-right asymmetry, A_N, and for the spin transfer, $D_{NN}^\Lambda$, in the single-spin process $p(\uparrow)p\to \Lambda K^+p$ and suggest further experimental tests of the proposed picture.Comment: 14 pages, 3 ps-figures. Phys. Rev. D (in press

Hyperon Polarization in the Constituent Quark Model

We consider mechanism for hyperon polarization in inclusive production. The main role belongs to the orbital angular momentum and polarization of the strange quark-antiquark pairs in the internal structure of the constituent quarks. We consider a nucleon as a core consisting of the constituent quarks embedded into quark condensate. The nonperturbative hadron structure is based on the results of chiral quark models.Comment: 14 pages, LaTeX, 2 Figures, References adde