Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review

Recent advancements in AI, especially deep learning, have contributed to a significant increase in the creation of new realistic-looking synthetic media (video, image, and audio) and manipulation of existing media, which has led to the creation of the new term ``deepfake''. Based on both the research literature and resources in English and in Chinese, this paper gives a comprehensive overview of deepfake, covering multiple important aspects of this emerging concept, including 1) different definitions, 2) commonly used performance metrics and standards, and 3) deepfake-related datasets, challenges, competitions and benchmarks. In addition, the paper also reports a meta-review of 12 selected deepfake-related survey papers published in 2020 and 2021, focusing not only on the mentioned aspects, but also on the analysis of key challenges and recommendations. We believe that this paper is the most comprehensive review of deepfake in terms of aspects covered, and the first one covering both the English and Chinese literature and sources.

A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception

In recent years there has been substantial growth in the capabilities of systems designed to generate text that mimics the fluency and coherence of human language. From this, there has been considerable research aimed at examining the potential uses of these natural language generators (NLG) towards a wide number of tasks. The increasing capabilities of powerful text generators to mimic human writing convincingly raises the potential for deception and other forms of dangerous misuse. As these systems improve, and it becomes ever harder to distinguish between human-written and machine-generated text, malicious actors could leverage these powerful NLG systems to a wide variety of ends, including the creation of fake news and misinformation, the generation of fake online product reviews, or via chatbots as means of convincing users to divulge private information. In this paper, we provide an overview of the NLG field via the identification and examination of 119 survey-like papers focused on NLG research. From these identified papers, we outline a proposed high-level taxonomy of the central concepts that constitute NLG, including the methods used to develop generalised NLG systems, the means by which these systems are evaluated, and the popular NLG tasks and subtasks that exist. In turn, we provide an overview and discussion of each of these items with respect to current research and offer an examination of the potential roles of NLG in deception and detection systems to counteract these threats. Moreover, we discuss the broader challenges of NLG, including the risks of bias that are often exhibited by existing text generation systems. This work offers a broad overview of the field of NLG with respect to its potential for misuse, aiming to provide a high-level understanding of this rapidly developing area of research.

Detection of Poisoning Attacks with Anomaly Detection in Federated Learning for Healthcare Applications: A Machine Learning Approach

The application of Federated Learning (FL) is steadily increasing, especially in privacy-aware applications, such as healthcare. However, its applications have been limited by security concerns due to various adversarial attacks, such as poisoning attacks (model and data poisoning). Such attacks attempt to poison the local models and data to manipulate the global models in order to obtain undue benefits and malicious use. Traditional methods of data auditing to mitigate poisoning attacks find their limited applications in FL because the edge devices never share their raw data directly due to privacy concerns, and are globally distributed with no insight into their training data. Thereafter, it is challenging to develop appropriate strategies to address such attacks and minimize their impact on the global model in federated learning. In order to address such challenges in FL, we proposed a novel framework to detect poisoning attacks using deep neural networks and support vector machines, in the form of anomaly without acquiring any direct access or information about the underlying training data of local edge devices. We illustrate and evaluate the proposed framework using different state of art poisoning attacks for two different healthcare applications: Electrocardiograph classification and human activity recognition. Our experimental analysis shows that the proposed method can efficiently detect poisoning attacks and can remove the identified poisoned updated from the global aggregation. Thereafter can increase the performance of the federated global.

Perspectives of Non-Expert Users on Cyber Security and Privacy: An Analysis of Online Discussions on Twitter

Current research on users` perspectives of cyber security and privacy related to traditional and smart devices at home is very active, but the focus is often more on specific modern devices such as mobile and smart IoT devices in a home context. In addition, most were based on smaller-scale empirical studies such as online surveys and interviews. We endeavour to fill these research gaps by conducting a larger-scale study based on a real-world dataset of 413,985 tweets posted by non-expert users on Twitter in six months of three consecutive years (January and February in 2019, 2020 and 2021). Two machine learning-based classifiers were developed to identify the 413,985 tweets. We analysed this dataset to understand non-expert users` cyber security and privacy perspectives, including the yearly trend and the impact of the COVID-19 pandemic. We applied topic modelling, sentiment analysis and qualitative analysis of selected tweets in the dataset, leading to various interesting findings. For instance, we observed a 54% increase in non-expert users` tweets on cyber security and/or privacy related topics in 2021, compared to before the start of global COVID-19 lockdowns (January 2019 to February 2020). We also observed an increased level of help-seeking tweets during the COVID-19 pandemic. Our analysis revealed a diverse range of topics discussed by non-expert users across the three years, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, and security and privacy issues involving different stakeholders. Overall negative sentiment was observed across almost all topics non-expert users discussed on Twitter in all the three years. Our results confirm the multi-faceted nature of non-expert users` perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on different facets of such perspectives.

Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators

Recently, there has been a rise in the development of powerful pre-trained natural language models, including GPT-2, Grover, and XLM. These models have shown state-of-the-art capabilities towards a variety of different NLP tasks, including question answering, content summarisation, and text generation. Alongside this, there have been many studies focused on online authorship attribution (AA). That is, the use of models to identify the authors of online texts. Given the power of natural language models in generating convincing texts, this paper examines the degree to which these language models can generate texts capable of deceiving online AA models. Experimenting with both blog and Twitter data, we utilise GPT-2 language models to generate texts using the existing posts of online users. We then examine whether these AI-based text generators are capable of mimicking authorial style to such a degree that they can deceive typical AA models. From this, we find that current AI-based text generators are able to successfully mimic authorship, showing capabilities towards this on both datasets. Our findings, in turn, highlight the current capacity of powerful natural language models to generate original online posts capable of mimicking authorial style sufficiently to deceive popular AA methods; a key finding given the proposed role of AA in real world applications such as spam-detection and forensic investigation.

Lightweight Transformer in Federated Setting for Human Activity Recognition

Human Activity Recognition (HAR) has been a challenging problem yet it needs to be solved. It will mainly be used for eldercare and healthcare as an assistive technology when ensemble with other technologies like Internet of Things(IoT). HAR can be achieved with the help of sensors, smartphones or images. Deep neural network techniques like artificial neural networks, convolutional neural networks and recurrent neural networks have been used in HAR, both in centralized and federated setting. However, these techniques have certain limitations. RNNs have limitation of parallelization, CNNS have the limitation of sequence length and they are computationally expensive. In this paper, to address the state of art challenges, we present a inertial sensors-based novel one patch transformer which gives the best of both RNNs and CNNs for Human activity recognition. We also design a testbed to collect real-time human activity data. The data collected is further used to train and test the proposed transformer. With the help of experiments, we show that the proposed transformer outperforms the state of art CNN and RNN based classifiers, both in federated and centralized setting. Moreover, the proposed transformer is computationally inexpensive as it uses very few parameter compared to the existing state of art CNN and RNN based classifier. Thus its more suitable for federated learning as it provides less communication and computational cost.

Out of the Shadows: Analyzing Anonymous' Twitter Resurgence during the 2020 Black Lives Matter Protests

Recently, there had been little notable activity from the once prominent hacktivist group, Anonymous. The group, responsible for activist-based cyber attacks on major businesses and governments, appeared to have fragmented after key members were arrested in 2013. In response to the major Black Lives Matter (BLM) protests that occurred after the killing of George Floyd, however, reports indicated that the group was back. To examine this apparent resurgence, we conduct a large-scale study of Anonymous affiliates on Twitter. To this end, we first use machine learning to identify a significant network of more than 33,000 Anonymous accounts. Through topic modelling of tweets collected from these accounts, we find evidence of sustained interest in topics related to BLM. We then use sentiment analysis on tweets focused on these topics, finding evidence of a united approach amongst the group, with positive tweets typically being used to express support towards BLM, and negative tweets typically being used to criticize police actions. Finally, we examine the presence of automation in the network, identifying indications of bot-like behavior across the majority of Anonymous accounts. These findings show that whilst the group has seen a resurgence during the protests, bot activity may be responsible for exaggerating the extent of this resurgence.

Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI

Deep learning play a vital role in classifying different arrhythmias using the electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and it can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a single silo. Additionally, deep learning models are like black-box, with no explainability of the predicted results, which is often required in clinical healthcare. This limits the application of deep learning in real-world health systems. In this paper, we design a new explainable artificial intelligence (XAI) based deep learning framework in a federated setting for ECG-based healthcare applications. The federated setting is used to solve issues such as data availability and privacy concerns. Furthermore, the proposed framework setting effectively classifies arrhythmia's using an autoencoder and a classifier, both based on a convolutional neural network (CNN). Additionally, we propose an XAI-based module on top of the proposed classifier to explain the classification results, which help clinical practitioners make quick and reliable decisions. The proposed framework was trained and tested using the MIT-BIH Arrhythmia database. The classifier achieved accuracy up to 94% and 98% for arrhythmia detection using noisy and clean data, respectively, with five-fold cross-validation.

The Shadowy Lives of Emojis: An Analysis of a Hacktivist Collective's Use of Emojis on Twitter

Emojis have established themselves as a popular means of communication in online messaging. Despite the apparent ubiquity in these image-based tokens, however, interpretation and ambiguity may allow for unique uses of emojis to appear. In this paper, we present the first examination of emoji usage by hacktivist groups via a study of the Anonymous collective on Twitter. This research aims to identify whether Anonymous affiliates have evolved their own approach to using emojis. To do this, we compare a large dataset of Anonymous tweets to a baseline tweet dataset from randomly sampled Twitter users using computational and qualitative analysis to compare their emoji usage. We utilise Word2Vec language models to examine the semantic relationships between emojis, identifying clear distinctions in the emoji-emoji relationships of Anonymous users. We then explore how emojis are used as a means of conveying emotions, finding that despite little commonality in emoji-emoji semantic ties, Anonymous emoji usage displays similar patterns of emotional purpose to the emojis of baseline Twitter users. Finally, we explore the textual context in which these emojis occur, finding that although similarities exist between the emoji usage of our Anonymous and baseline Twitter datasets, Anonymous users appear to have adopted more specific interpretations of certain emojis. This includes the use of emojis as a means of expressing adoration and infatuation towards notable Anonymous affiliates. These findings indicate that emojis appear to retain a considerable degree of similarity within Anonymous accounts as compared to more typical Twitter users. However, their are signs that emoji usage in Anonymous accounts has evolved somewhat, gaining additional group-specific associations that reveal new insights into the behaviours of this unusual collective.

Behind the Mask: A Computational Study of Anonymous' Presence on Twitter

The hacktivist group Anonymous is unusual in its public-facing nature. Unlike other cybercriminal groups, which rely on secrecy and privacy for protection, Anonymous is prevalent on the social media site, Twitter. In this paper we re-examine some key findings reported in previous small-scale qualitative studies of the group using a large-scale computational analysis of Anonymous' presence on Twitter. We specifically refer to reports which reject the group's claims of leaderlessness, and indicate a fracturing of the group after the arrests of prominent members in 2011-2013. In our research, we present the first attempts to use machine learning to identify and analyse the presence of a network of over 20,000 Anonymous accounts spanning from 2008-2019 on the Twitter platform. In turn, this research utilises social network analysis (SNA) and centrality measures to examine the distribution of influence within this large network, identifying the presence of a small number of highly influential accounts. Moreover, we present the first study of tweets from some of the identified key influencer accounts and, through the use of topic modelling, demonstrate a similarity in overarching subjects of discussion between these prominent accounts. These findings provide robust, quantitative evidence to support the claims of smaller-scale, qualitative studies of the Anonymous collective.