Sungkyunkwan University · 情報科学
Professor Shahroz Tariq's research lab specializes in cybersecurity and artificial intelligence, with a focus on detecting deepfakes, adversarial attacks, and anomalies in critical systems. The lab develops advanced machine learning and deep learning techniques—particularly neural networks, transfer learning, and ensemble methods—for identifying fake media, securing in-vehicle networks (CAN bus), and ensuring system integrity in space and metaverse environments. A key emphasis is on building generalizable, data-efficient detection systems that work across diverse attack types and real-world scenarios.
Figures are computed from collected data and may differ slightly.
Due to the significant advancements in image processing and machine learning algorithms, it is much easier to create, edit, and produce high quality images. However, attackers can maliciously use these tools to create legitimate looking but fake images to harm others, bypass image detection algorithms, or fool image recognition classifiers. In this work, we propose neural network based classifiers to detect fake human faces created by both 1) machines and 2) humans. We use ensemble methods to de
Detecting an anomaly is not only important for many terrestrial applications on Earth but also for space applications. Especially, satellite missions are highly risky because unexpected hardware and software failures can occur due to sudden or unforeseen space environment changes. Anomaly detection and spacecraft health monitoring systems have heavily relied on human expertise to investigate whether they are a true anomaly or not. Also, it is practically infeasible to produce labels on data due
In-vehicle communications, due to simplicity and reliability, a Controller Area Network (CAN) bus is widely used as the de facto standard to provide serial communications between Electronic Control Units (ECUs). However, prior research exhibits several network-level attacks can be easily performed and exploited in the CAN bus. Additionally, new types of intrusion attacks are discovered very frequently. However, unless we have a large amount of data about an intrusion, developing an efficient dee
Deep learning-based video manipulation methods have become widely accessible to the masses. With little to no effort, people can quickly learn how to generate deepfake (DF) videos. While deep learning-based detection methods have been proposed to identify specific types of DFs, their performance suffers for other types of deepfake methods, including real-world deepfakes, on which they are not sufficiently trained. In other words, most of the proposed deep learning-based detection methods lack tr
Creating fake images such as replacing one's face with other person's face has become much easier due to the advancement of sophisticated image editing tools. In addition, Generative Adversarial Networks (GANs) enable creating natural looking human faces. However, fake images can cause many potential problems, as they can be misused to abuse information, hurt people, and generate fake identification. Therefore, detecting fake face images is critical for protecting individuals from various misuse
The metaverse has gained significant attention from various industries due to its potential to create a fully immersive and interactive virtual world. However, the integration of deepfakes in the metaverse brings serious security implications, particularly with regard to impersonation. This paper examines the security implications of deepfakes in the metaverse, specifically in the context of gaming, online meetings, and virtual offices. The paper discusses how deepfakes can be used to impersonat
Recent advancements in web-based multimedia technologies, such as face recognition web services powered by deep learning, have been significant. As a result, companies such as Microsoft, Amazon, and Naver provide highly accurate commercial face recognition web services for a variety of multimedia applications. Naturally, such technologies face persistent threats, as virtually anyone with access to deepfakes can quickly launch impersonation attacks. These attacks pose a serious threat to authenti
A security operations centre (SOC) is a facility where teams of security professionals, supported by advanced technologies and processes, work together to monitor, detect, and respond to cybersecurity incidents. With advances in AI technology, most of the SOC functions are increasingly becoming AI-driven. Among these, real-time alert monitoring and triage is particularly important. Recent studies, by both industry and academia, have highlighted the problem of alert fatigue and burnout in SOC. Se
Recently, significant advancements have been made in face recognition technologies using Deep Neural Networks. As a result, companies such as Microsoft, Amazon, and Naver offer highly accurate commercial face recognition web services for diverse applications to meet the end-user needs. Naturally, however, such technologies are threatened persistently, as virtually any individual can quickly implement impersonation attacks. In particular, these attacks can be a significant threat for authenticati
In recent years, deep learning-based video manipulation methods have become widely accessible to masses. With little to no effort, people can easily learn how to generate deepfake videos with only a few victims or target images. This creates a significant social problem for everyone whose photos are publicly available on the Internet, especially on social media websites. Several deep learning-based detection methods have been developed to identify these deepfakes. However, these methods lack gen
We explored the problems which are soon to be faced while parking autonomous cars in parking lots. Like where is the closest parking slot available to the autonomous car? How to navigate to that location? What kind of parking structures could be good for autonomous cars? We also provide an initial solution which uses a central server and the graph of the parking lot to guide the cars to the closest parking slots. With experiments, we have shown that our proposed method should be effective for th
Deep learning-based video manipulation methods have become widely accessible to the masses. With little to no effort, people can quickly learn how to generate deepfake (DF) videos. While deep learning-based detection methods have been proposed to identify specific types of DFs, their performance suffers for other types of deepfake methods, including real-world deepfakes, on which they are not sufficiently trained. In other words, most of the proposed deep learning-based detection methods lack tr
We evaluate the racial bias in face recognition application programming interfaces (APIs) using real and deepfake celebrity images. We use deepfake generation methods to introduce small, imperceptible changes to the real images to shift the racial class of predictions, showing how deepfake images exacerbated racial bias in web-based face recognition APIs.
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