53 research outputs found
Impacts and Risk of Generative AI Technology on Cyber Defense
Generative Artificial Intelligence (GenAI) has emerged as a powerful
technology capable of autonomously producing highly realistic content in
various domains, such as text, images, audio, and videos. With its potential
for positive applications in creative arts, content generation, virtual
assistants, and data synthesis, GenAI has garnered significant attention and
adoption. However, the increasing adoption of GenAI raises concerns about its
potential misuse for crafting convincing phishing emails, generating
disinformation through deepfake videos, and spreading misinformation via
authentic-looking social media posts, posing a new set of challenges and risks
in the realm of cybersecurity. To combat the threats posed by GenAI, we propose
leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of
cyberattacks, as a foundational model for cyber defense. This paper aims to
provide a comprehensive analysis of the risk areas introduced by the offensive
use of GenAI techniques in each phase of the CKC framework. We also analyze the
strategies employed by threat actors and examine their utilization throughout
different phases of the CKC, highlighting the implications for cyber defense.
Additionally, we propose GenAI-enabled defense strategies that are both
attack-aware and adaptive. These strategies encompass various techniques such
as detection, deception, and adversarial training, among others, aiming to
effectively mitigate the risks posed by GenAI-induced cyber threats
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization
Automatic speech recognition (ASR) has recently become an important challenge
when using deep learning (DL). It requires large-scale training datasets and
high computational and storage resources. Moreover, DL techniques and machine
learning (ML) approaches in general, hypothesize that training and testing data
come from the same domain, with the same input feature space and data
distribution characteristics. This assumption, however, is not applicable in
some real-world artificial intelligence (AI) applications. Moreover, there are
situations where gathering real data is challenging, expensive, or rarely
occurring, which can not meet the data requirements of DL models. deep transfer
learning (DTL) has been introduced to overcome these issues, which helps
develop high-performing models using real datasets that are small or slightly
different but related to the training data. This paper presents a comprehensive
survey of DTL-based ASR frameworks to shed light on the latest developments and
helps academics and professionals understand current challenges. Specifically,
after presenting the DTL background, a well-designed taxonomy is adopted to
inform the state-of-the-art. A critical analysis is then conducted to identify
the limitations and advantages of each framework. Moving on, a comparative
study is introduced to highlight the current challenges before deriving
opportunities for future research
Backdoor Attacks and Countermeasures in Natural Language Processing Models: A Comprehensive Security Review
Deep Neural Networks (DNNs) have led to unprecedented progress in various
natural language processing (NLP) tasks. Owing to limited data and computation
resources, using third-party data and models has become a new paradigm for
adapting various tasks. However, research shows that it has some potential
security vulnerabilities because attackers can manipulate the training process
and data source. Such a way can set specific triggers, making the model exhibit
expected behaviors that have little inferior influence on the model's
performance for primitive tasks, called backdoor attacks. Hence, it could have
dire consequences, especially considering that the backdoor attack surfaces are
broad.
To get a precise grasp and understanding of this problem, a systematic and
comprehensive review is required to confront various security challenges from
different phases and attack purposes. Additionally, there is a dearth of
analysis and comparison of the various emerging backdoor countermeasures in
this situation. In this paper, we conduct a timely review of backdoor attacks
and countermeasures to sound the red alarm for the NLP security community.
According to the affected stage of the machine learning pipeline, the attack
surfaces are recognized to be wide and then formalized into three
categorizations: attacking pre-trained model with fine-tuning (APMF) or
prompt-tuning (APMP), and attacking final model with training (AFMT), where
AFMT can be subdivided into different attack aims. Thus, attacks under each
categorization are combed. The countermeasures are categorized into two general
classes: sample inspection and model inspection. Overall, the research on the
defense side is far behind the attack side, and there is no single defense that
can prevent all types of backdoor attacks. An attacker can intelligently bypass
existing defenses with a more invisible attack. ......Comment: 24 pages, 4 figure
Interpretable Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models
We propose a novel anomaly detection method for echocardiogram videos. The
introduced method takes advantage of the periodic nature of the heart cycle to
learn three variants of a variational latent trajectory model (TVAE). While the
first two variants (TVAE-C and TVAE-R) model strict periodic movements of the
heart, the third (TVAE-S) is more general and allows shifts in the spatial
representation throughout the video. All models are trained on the healthy
samples of a novel in-house dataset of infant echocardiogram videos consisting
of multiple chamber views to learn a normative prior of the healthy population.
During inference, maximum a posteriori (MAP) based anomaly detection is
performed to detect out-of-distribution samples in our dataset. The proposed
method reliably identifies severe congenital heart defects, such as Ebstein's
Anomaly or Shone-complex. Moreover, it achieves superior performance over
MAP-based anomaly detection with standard variational autoencoders when
detecting pulmonary hypertension and right ventricular dilation. Finally, we
demonstrate that the proposed method enables interpretable explanations of its
output through heatmaps highlighting the regions corresponding to anomalous
heart structures.Comment: accepted at IMLH workshop ICML 202
Multimedia Forensics
This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field
Persistent Homology Tools for Image Analysis
Topological Data Analysis (TDA) is a new field of mathematics emerged rapidly since the first decade of the century from various works of algebraic topology and
geometry. The goal of TDA and its main tool of persistent homology (PH) is to provide topological insight into complex and high dimensional datasets. We take this
premise onboard to get more topological insight from digital image analysis and quantify tiny low-level distortion that are undetectable except possibly by highly trained persons. Such image distortion could be caused intentionally (e.g. by morphing and steganography) or naturally in abnormal human tissue/organ scan images as a result of onset of cancer or other diseases.
The main objective of this thesis is to design new image analysis tools based on persistent homological invariants representing simplicial complexes on sets of pixel landmarks over a sequence of distance resolutions. We first start by proposing innovative automatic techniques to select image pixel landmarks to build a variety of
simplicial topologies from a single image. Effectiveness of each image landmark selection demonstrated by testing on different image tampering problems such as morphed face detection, steganalysis and breast tumour detection.
Vietoris-Rips simplicial complexes constructed based on the image landmarks at an increasing distance threshold and topological (homological) features computed at each threshold and summarized in a form known as persistent barcodes. We vectorise the space of persistent barcodes using a technique known as persistent binning where we demonstrated the strength of it for various image analysis purposes. Different machine learning approaches are adopted to develop automatic detection of tiny
texture distortion in many image analysis applications. Homological invariants used in this thesis are the 0 and 1 dimensional Betti numbers. We developed an innovative approach to design persistent homology (PH) based
algorithms for automatic detection of the above described types of image distortion. In particular, we developed the first PH-detector of morphing attacks on passport face biometric images. We shall demonstrate significant accuracy of 2 such morph detection algorithms with 4 types of automatically extracted image landmarks: Local Binary patterns (LBP), 8-neighbour super-pixels (8NSP), Radial-LBP (R-LBP) and centre-symmetric LBP (CS-LBP). Using any of these techniques yields several persistent barcodes that summarise persistent topological features that help gaining insights into complex hidden structures not amenable by other image analysis methods. We shall also demonstrate significant success of a similarly developed PH-based universal steganalysis tool capable for the detection of secret messages hidden inside digital images. We also argue through a pilot study that building PH records from digital images can differentiate breast malignant tumours from benign tumours using digital mammographic images. The research presented in this thesis creates new opportunities to build real applications based on TDA and demonstrate many research challenges in a variety of image processing/analysis tasks. For example, we describe a TDA-based exemplar image inpainting technique (TEBI), superior to existing exemplar algorithm, for the reconstruction of missing image regions
Multimedia Forensics
This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field
- …