Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks

Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model. Empirical evidence for transferability has been shown in previous work, but the underlying reasons why an attack transfers or not are not yet well understood. In this paper, we present a comprehensive analysis aimed to investigate the transferability of both test-time evasion and training-time poisoning attacks. We provide a unifying optimization framework for evasion and poisoning attacks, and a formal definition of transferability of such attacks. We highlight two main factors contributing to attack transferability: the intrinsic adversarial vulnerability of the target model, and the complexity of the surrogate model used to optimize the attack. Based on these insights, we define three metrics that impact an attack's transferability. Interestingly, our results derived from theoretical analysis hold for both evasion and poisoning attacks, and are confirmed experimentally using a wide range of linear and non-linear classifiers and datasets

Detecting AI generated text using neural networks

For humans, distinguishing machine generated text from human written text is men- tally taxing and slow. NLP models have been created to do this more effectively and faster. But, what if some adversarial changes have been added to the machine generated text? This thesis discusses this issue and text detectors in general. The primary goal of this thesis is to describe the current state of text detectors in research and to discuss a key adversarial issue in modern NLP transformers. To describe the current state of text detectors a Systematic Literature Review was done on 50 relevant papers to machine-centric detection in chapter 2. As for the key ad- versarial issue, chapter 3 describes an experiment where RoBERTa was used to test transformers against simple mutations which cause mislabelling. The state of the literature was written at length in the 2nd chapter, showing how viable text detection as a subject has become. Lastly, RoBERTa was shown to be vulnerable to mutation attacks. The solution was found to be fine-tuning it to some heuristics, as long as the mutations can be predicted the model can be fine tuned to detect them

Countering Expansion and Organization of Terrorism in Cyberspace

Terrorists use cyberspace and social media technology to create fear and spread violent ideologies, which pose a significant threat to public security. Researchers have documented the importance of the application of law and regulation in dealing with the criminal activities perpetrated through the aid of computers in cyberspace. Using routine activity theory, this study assessed the effectiveness of technological approaches to mitigating the expansion and organization of terrorism in cyberspace. The study aligned with the purpose area analysis objective of classifying and assessing potential terrorist threats to preempt and mitigate the attacks. Data collection included document content analysis of the open-source documents, government threat assessments, legislation, policy papers, and peer-reviewed academic literature and semistructured interviews with fifteen security experts in Nigeria. Yin\u27s recommended analysis process of iterative and repetitive review of materials was applied to the documents analysis, including interviews of key public and private sector individuals to identify key themes on Nigeria\u27s current effort to secure the nation\u27s cyberspace. The key findings were that the new generation of terrorists who are more technological savvy are growing, cybersecurity technologies are effective and quicker tools, and bilateral/multilateral cooperation is essential to combat the expansion of terrorism in cyberspace. The implementation of recommendations from this study will improve the security in cyberspace, thereby contributing to positive social change. The data provided may be useful to stakeholders responsible for national security, counterterrorism, law enforcement on the choice of cybersecurity technologies to confront terrorist expansion, and organization in cyberspace

Customer Xperience - Using Social Media Data to Drive Actionable Insights for Retail

Utilizadores de redes sociais produzem, diariamente, uma quantidade substancial de dados. É proposta uma solução capaz de extrair dados relevantes, processar, analisar e gerar conclusões accionáveis para o apoio de actividades de retalho. Estas actividades compreendem mas não se restringem a: fixação de preços, experiência do cliente, planeamento, alocação de recursos, campanhas promocionais e disposição de loja. Para vias de conceito de prova foi utilizada a plataforma Twitter como fonte de dados. Estes foram seleccionados e extraídos com recurso inquéritos que garantem a relevância dos dados que são passados para análise. São utilizadas ferramentas de processamento de linguagem natural para extrair dos dados textuais entidades, aspectos destas entidades, relações entre estes elementos e por fim sentimento e emoção presente no texto referente a estes elementos. É levantado o contexto em que surge a submissão para a plataforma, agregando dados sobre o utilizador e sobre a recepção da sua comunicação textual pelos demais utilizadores da plataforma. Deste processo pretende-se revelar padrões, tanto quanto aos assuntos sobre os quais os utilizadores se expressam, tal como o seu posicionamento emocional em relação aos ditos assuntos. É da leitura destes padrões que se pretende extrair conclusões accionáveis para apoiar as actividade acima nomeadas.Social media users produce substancial amounts of data on a daily basis. In this project, a solution is offered that allows for extraction, processing and analysis on this data in order to generate actionable insights for retail activities, including but not limited to: Pricing, Store Layout, Customer Experience, Targeted Campaigns, Allocation of stock, and Planning. For proof of concept, the Twitter platform was used as the primary data source. Data was selected based on queries so that only relevant data is extracted. User submitted text was processed using natural language processing (NLP) in order to extract entities and aspects of such entities, as well as relations between entities. Sentiment analysis and emotion detection is performed on the elements generated through NLP and context is understood around the user's submission. This is done by gathering data about the user as well as about the reception of the post by other users on the network. From this process, patterns start to emerge regarding subjects being talked about and sentiment polarity regarding those subjects. From these patterns actionable insights is extracted to drive the activities mentioned abov