MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense

Present attack methods can make state-of-the-art classification systems based on deep neural networks misclassify every adversarially modified test example. The design of general defense strategies against a wide range of such attacks still remains a challenging problem. In this paper, we draw inspiration from the fields of cybersecurity and multi-agent systems and propose to leverage the concept of Moving Target Defense (MTD) in designing a meta-defense for 'boosting' the robustness of an ensemble of deep neural networks (DNNs) for visual classification tasks against such adversarial attacks. To classify an input image, a trained network is picked randomly from this set of networks by formulating the interaction between a Defender (who hosts the classification networks) and their (Legitimate and Malicious) users as a Bayesian Stackelberg Game (BSG). We empirically show that this approach, MTDeep, reduces misclassification on perturbed images in various datasets such as MNIST, FashionMNIST, and ImageNet while maintaining high classification accuracy on legitimate test images. We then demonstrate that our framework, being the first meta-defense technique, can be used in conjunction with any existing defense mechanism to provide more resilience against adversarial attacks that can be afforded by these defense mechanisms. Lastly, to quantify the increase in robustness of an ensemble-based classification system when we use MTDeep, we analyze the properties of a set of DNNs and introduce the concept of differential immunity that formalizes the notion of attack transferability.Comment: Accepted to the Conference on Decision and Game Theory for Security (GameSec), 201

A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?

The ability to detect unfamiliar or unexpected images is essential for safe deployment of computer vision systems. In the context of classification, the task of detecting images outside of a model's training domain is known as out-of-distribution (OOD) detection. While there has been a growing research interest in developing post-hoc OOD detection methods, there has been comparably little discussion around how these methods perform when the underlying classifier is not trained on a clean, carefully curated dataset. In this work, we take a closer look at 20 state-of-the-art OOD detection methods in the (more realistic) scenario where the labels used to train the underlying classifier are unreliable (e.g. crowd-sourced or web-scraped labels). Extensive experiments across different datasets, noise types & levels, architectures and checkpointing strategies provide insights into the effect of class label noise on OOD detection, and show that poor separation between incorrectly classified ID samples vs. OOD samples is an overlooked yet important limitation of existing methods. Code: https://github.com/glhr/ood-labelnoiseComment: Accepted at CVPR 202

ASTRA: An Action Spotting TRAnsformer for Soccer Videos

In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a Transformer encoder-decoder architecture to achieve the desired output temporal resolution and to produce precise predictions, (b) a balanced mixup strategy to handle the long-tail distribution of the data, (c) an uncertainty-aware displacement head to capture the label variability, and (d) input audio signal to enhance detection of non-visible actions. Results demonstrate the effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set. Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd position with an Average-mAP of 70.21 on the challenge set

Teaching intervention to enhance HIV infection awareness in a biomedical science degree

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Condom use remains the predominant prophylactic intervention to control rates of human immunodeficiency virus (HIV) infection. However, chemoprophylactic strategies, which involve pre-exposure prophyaxis (PrEP) and post-exposure prophyaxis (PEP), have emerged as appropriate prevention tools to minimise and prevent future infections. Different studies have indicated that PrEP can prevent new HIV infections among men who have sex with men when used daily or event-based, and it is also effective with heterosexuals and people who inject drugs. However, appropriate education is needed as recent reports have observed a decline in adherence to PrEP over time, particularly in young adults, which will impact on the effectiveness of PrEP. Thus, we created a brief educational short intervention (3 hours) to increase the awareness of HIV with second year BMedSci Medical Science (Hons) students at De Montfort University (DMU, UK) in 2016/17 (Peña-Fernández et al., 2017). Briefly, BMedSci students tailored a community-centred intervention programme to reduce HIV infection rates following evidence-based public health methodology. 92% indicated an acquisition of knowledge for preventing HIV transmission and tools to fight this disease. However, BMedSci students also showed a lack of knowledge of preventative measures (PrEP and PEP), routes of transmission and appropriate screening. We implemented a similar teaching strategy with BSc Biomedical Science (BMS) students enrolled in the level 4 module of Basic Microbiology in 2017/18, but limited to two hours: one-hour lecture and one hour workshop in which different HIV prevention strategies were discussed and analysed by students. BMS students were also provided with an overview about the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90:90:90 targets in the UK (2016). In a similar way as with the BMedSci cohort, BMS students showed little awareness about PEP/PrEP, specifically knowledge about what are they/how they work, access and usage. This teaching intervention was well-received by students according to the feedback provided in the final module level feedback. BMS participants (n=27 out of 187 students) indicated that they enjoyed the session and suggested a practical session and the introduction of case studies to enhance the teaching intervention. We are developing a virtual clinical case study on HIV following recent successful experiences in the development and introduction of these novel learning strategies and have performed small modifications in the delivery of this workshop for 2018/19 to increase engagement and interaction. In conclusion, we consider that similar short education interventions that specifically target HIV chemoprophylaxis would be needed in any degree to prevent the decline in adherence to PrEP over time observed in young adults and reduce PEP/PrEP stigma and other barriers which could impede their access

Estudio de las interacciones entre el sulfatiazol y la mezcla dioxano-agua

Se ha realizado un estudio de interacciones soluto-disolvente para el sulfatiazol en dioxanp-agua a partir de medidas experimentales de solubilidad en el mencionado sistema disolvente y de la determinación de la solubilidad ideal de la sufamida mediante medidas calorimétricas. Si la sulfamida formase una disolución ideal la máxima solubilidad encontrada debería ser igual a la ideal; sin embargo, el hecho de encontrar solubilidades experimentales menores indica que se trata de una solución regular en la que el fármaco y el disolvente, o ambos, se asocian predominantemente consigo mismo por lo que las solubilidades experimentales encontradas en todos los casos son inferiores a la solubilidad ideal. Los valores del parámetro de Walker calculados, son para proporciones de mezclas tanto menores como mayores de la unidad. Se considera que en el primer caso tanto el soluto como el disolvente, o ambos, se asocian entre sí, y por lo tanto la interacción soluto-disolvente real es inferior a la ideal. Para el segundo caso. se puede interpretar esta situación como una débil solubilización del soluto

Estudio de especialistas egresados en Farmacia Industrial y Galénica de la Universidad de Alcalá

La Especialidad de Farmacia Industrial y Galénica de la Universidad de Alcalá, es una Especialidad en Ciencias de la Salud en régimen de alumnado (RD 127/1984 de 15 de octubre). Comenzó su puesta en marcha en la Universidad de Alcalá en el año 2005 continuando en la actualidad. Esta Especialidad se imparte por profesores universitarios y cuenta además, con la inestimable colaboración de expertos de la Asociación Española de Farmacéuticos de la Industria, Farmaindustria, laboratorios farmacéuticos, profesionales de la Agencia Española de Medicamentos y Productos Sanitarios e inspectores de la Comunidad de Madrid. En este trabajo, se pretende mostrar la experiencia profesional de los alumnos egresados de ocho promociones así como el nivel de aceptación con respecto a la organización y desarrollo del curso, las instalaciones, infraestructura y recursos utilizados, la calidad y motivación del profesorado. Los resultados obtenidos muestran la elevada adquisición de destrezas y habilidades de todos los estudiantes y su alto nivel de formación para acceder a un puesto de trabajo en la industria farmacéutica y otras instituciones o empresas tras cursar la Especialidad de Farmacia Industrial y Galénica. Se pone de manifiesto también la adecuación de las infraestructuras y recursos de nuestra Universidad