Dr. Maryam Ramezanzadehmoghadam

With the rapid growth of social applications, the misuse of synthetic voice generation technologies poses a significant security threat. Voice spoofing, where artificial voices are generated to impersonate real individuals, is a growing concern in various domains, including online communication, authentication, and social media interactions. This paper uses deep learning techniques to present a forensic investigation into the detection of synthetic voice spoofing within social apps. This study integrates a Convolutional Neural Network (CNN) with a Temporal Convolutional Network (TCN) in a hybrid architecture. A lightweight MobileNet CNN first extracts spatial features from Mel-Spectrograms, which are then analyzed by the TCN to capture sequential patterns. Using the fake-or-real (FoR) dataset, the for-norm dataset, this model achieved a training precision of 99.89% and validation accuracy of 99.79% and for-rerec dataset the model achieved a training precision of 99.79% and validation accuracy of 94.22%. Evaluation metrics, including the precision-recall curve with an average precision of 99% and the ROC curve with an AUC of 99%, underscore the model's robustness in distinguishing real from synthetic audio, offering a reliable solution for real-time deployment in resource-constrained environments.

Academic advising plays a crucial role in fostering student success. However, challenges such as limited advisor availability can hinder effective support. Generative AI, particularly AI-powered chatbots, offers the potential to enhance student advising in higher education by providing personalized guidance. These technologies help college students find the information and resources needed to create degree plans aligned with their academic goals. This research introduces ARGObot, an intelligent advising system that facilitates student navigation of university policies through automated interpretation of the student handbook as its primary knowledge base. ARGObot enhances accessibility to critical academic policies and procedures, supporting incoming students' success through personalized guidance. Our system integrates a multifunctional agent enhanced by a Large Language Model (LLM). The architecture employs multiple external tools to enhance its capabilities: a Retrieval-Augmented Generation (RAG) system accesses verified university sources; email integration facilitates Human-in-the-Loop (HITL) interaction; and a web search function expands the system's knowledge base beyond predefined constraints. This approach enables the system to provide contextually relevant and verified responses to various student queries. This architecture evolved from our initial implementation based on Gemini 1 Pro, which revealed significant limitations due to its lack of agent-based functionality, resulting in hallucination issues and irrelevant responses. Subsequent evaluation demonstrated that our enhanced version, integrating GPT-4 with the text-embedding-ada-002 model, achieved superior performance across all metrics. This paper also presents a comparative analysis of both implementations, highlighting the architectural improvements and their impact on system performance.

Despite the proven efficacy of large language models (LLMs) like GPT in numerous applications, concerns have emerged regarding their exploitation in creating phishing emails or network intrusions, which have shown to be detrimental. The multimodal functionalities of large vision-language models (LVLMs) enable them to grasp visual commonsense knowledge. This study investigates the feasibility of using two widely available commercial LVLMs, LLAVA, and multimodal GPT4, for effectively bypassing CAPTCHAs or producing bot-driven fraud through malicious prompts. It was found that these LVLMs can interpret and respond to the visual information presented in image, puzzle, and text-based CAPTCHA and reCAPTCHA, thereby potentially circumventing the challenge-response authentication security measure. This capability suggests that such systems could facilitate unauthorized access to secured accounts via remote digital methods. Remarkably, these attacks can be executed with the standard, unaltered versions of the LVLMs, eliminating the need for previous adversarial methods like jailbreaking.

Developers and quality assurance testers often rely on manual testing to test accessibility features throughout the product lifecycle. Unfortunately, manual testing can be tedious, often has an overwhelming scope, and can be difficult to schedule amongst other development milestones. Recently, Large Language Models (LLMs) have been used for a variety of tasks including automation of UIs. However, to our knowledge, no one has yet explored the use of LLMs in controlling assistive technologies for the purposes of supporting accessibility testing. In this paper, we explore the requirements of a natural language based accessibility testing workflow, starting with a formative study. From this we build a system that takes a manual accessibility test instruction in natural language (e.g., “Search for a show in VoiceOver”) as input and uses an LLM combined with pixel-based UI Understanding models to execute the test and produce a chaptered, navigable video. In each video, to help QA testers, we apply heuristics to detect and flag accessibility issues (e.g., Text size not increasing with Large Text enabled, VoiceOver navigation loops). We evaluate this system through a 10-participant user study with accessibility QA professionals who indicated that the tool would be very useful in their current work and performed tests similarly to how they would manually test the features. The study also reveals insights for future work on using LLMs for accessibility testing.

Students, especially those outside the field of cybersecurity, are increasingly turning to Large Language Model (LLM)-based generative AI tools for coding assistance. These AI code generators provide valuable support to developers by generating code based on provided input and instructions. However, the quality and accuracy of the generated code can vary, depending on factors such as task complexity, the clarity of instructions, and the model’s familiarity with the programming language. Additionally, these generated codes may inadvertently utilize vulnerable built-in functions, potentially leading to source code vulnerabilities and exploits. This research undertakes an in-depth analysis and comparison of code generation, code completion, and security suggestions offered by prominent AI models, including OpenAI CodeX, CodeBert, and ChatGPT. The research aims to evaluate the effectiveness and security aspects of these tools in terms of their code generation, code completion capabilities, and their ability to enhance security. This analysis serves as a valuable resource for developers, enabling them to proactively avoid introducing security vulnerabilities in their projects. By doing so, developers can significantly reduce the need for extensive revisions and resource allocation, whether in the short or long term.

The rise of Blockchain technology has been rapid and is becoming more popular now than ever before. Blockchain technology is mostly known for being the framework for cryptocurrencies such as Bitcoin, but it can be applied to many areas and industries other than cryptocurrencies. Blockchain technology has managed to provide security, transparency, immutability, decentralization which help in keeping valuable data in place. Understanding blockchain is an essential part of cybersecurity professionals. However, with the ever-increasing demand for cybersecurity students to learn blockchain technologies, there are few hands-on labs/modules available for training current IT students, the future cybersecurity professionals. The objective is to develop a series of novel hands-on labs that would fit individual students’ needs for blockchain in various real-life applications.
The goal of this study is to educate current IT/cybersecurity students on the application of Blockchain in supply chain, digital evidence and non-fungible tokens (NFTs) through hands-on labs, which is helpful to integrate blockchain technologies into the current cybersecurity curriculum. These hands-on labs explore fundamental knowledge, such as the cryptographical computations and hashing algorithms behind blockchain. Meanwhile, students will be exposed to learning how to use the Ethereum platform, Remix, to create a smart contract and write codes in Solidity. In addition, the hands-on labs focus on asset and shipment tracking and decentralized storage for digital evidence through blockchain techniques.

With the increased adaptation of natural language processing models in industrial applications such as hiring and recruitment, chatbots, social media monitoring, and targeted advertising, pretrained language models (PTM) need fair and equal behavior across all ranges of demographic groups. ELECTRA has substantially outperformed BERT by predicting the original identities of the corrupted tokens over all input tokens rather than just the small subset that was masked out. Considering such enhancement and the 1/4 less amount of computing required by ELECTRA, it can be one of the most suitable industrial applications. Therefore, it is crucial to understand its underlying architecture and tokenization protocol to identify any potential discrimination towards specific groups. This paper presents a fair operation from ELECTRAs' pretrained network that shows the accurate classification of token replacements. This result is achieved via using a dataset with racially and gender-associated personal names, finetuning ELECTRA with the general language understanding evaluation (GLUE) benchmark, which analyzes the interactions of encoders and decoders using the Contextualized Embedding Association Test (CEAT) and sentiment association test. In addition, this paper will demonstrate that ELECTRA can achieve Bias-aware Fair prediction with higher accuracy on downstream tasks after fully trained. This project is investigating the prediction of generator and discriminator on an initial word's token using the Named Entity Recognition (NER), and Part of Speech tagging (POS)

By recording instances of significant forensic relevance, smartphones, which are becoming increasingly crucial for documenting ordinary life events, can produce pieces of evidence in court. Due to privacy or other issues, not everyone is open to having all the data on their phone collected and analyzed. In addition, Law Enforcement Organizations need a lot of memory to keep the information taken from a witness's phone. Deepfakes which are purposefully utilized as a source of disinformation, manipulation, harassment, and persuasion in court, present another significant problem for law enforcement organizations. Recently, the introduction of blockchain has altered the way we conduct business. Decentralized Applications (Dapps) may be a fantastic way to verify the accuracy of the data, stop the spread of false information, extract specific data with precision, and offer a framework for sharing that takes into account privacy and memory issues. This article outlines the creation of a Dapp that provides users with a secure conduit through distributing evidence that has been verified. By utilizing machine learning (ML) classifiers, this platform not only distinguishes between altered and original material before allowing it, but also uses user-uploaded media to retrain its models to increase prediction accuracy and offer complete transparency. The end outcome of this activity can maintain a clear record (timestamp) of the occurrence, submitted proof, and helpful metadata with the aid of the blockchains' consensus notion.

EXCERPT: Given the rise of digital technology and communication, there’s a higher chance of smartphones containing shreds of evidence related to an incident. The variety of digital evidence sources, creation and sharing of information, and incidents within forums, and other Online broadcasting medium poses new and challenging problems for digital investigators. Three of the most significant obstacles are as follows: 1) Authentication of the evidence, 2) Acquisition 3) Storage and analysis. Blockchain, by offering a decentralized
network and an IPFS hash storage system, can be a great solution to the acquisition and storage challenges. Machine learning (ML), as one of the leading solutions to the identification and authentication of evidence, can provide the best performance in the detection of deepfake media. Our proposed framework, by combining machine learning and the decentralized nature of Dapps is designed to offer authenticity, immutability, traceability, robustness, and distributed trust between evidence entitles and examiners. To be able to keep the storage cost and resources minimal, avoid the whole process of consent/warrant form, extract the relevant data only, our implementation is based on the assumption of voluntary media upload
by those who were present at the crime scene.

Deepfakes are realistic-looking fake media generated by deep-learning algorithms that iterate through large datasets until they have learned how to solve the given problem (i.e., swap faces or objects in video and digital content). The massive generation of such content and modification technologies is rapidly affecting the quality of public discourse and the safeguarding of human rights. Deepfakes are being widely used as a malicious source of misinformation in court that seek to sway a court’s decision. Because digital evidence is critical to the outcome of many legal cases, detecting deepfake media is extremely important and in high demand in digital forensics. As such, it is important to identify and build a classifier that can accurately distinguish between authentic and disguised media, especially in facial-recognition systems as it can be used in identity protection too. In this work, we compare the most common, state-of-the-art face-detection classifiers such as Custom CNN, VGG19, and DenseNet-121 using an augmented real and fake face-detection dataset. Data augmentation is used to boost performance and reduce computational resources. Our preliminary results indicate that VGG19 has the best performance and highest accuracy of 95% when compared with other analyzed models.