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With ChatGPT under the spotlight, utilizing large language models (LLMs) to assist academic writing has drawn a significant amount of debate in the community. In this paper, we aim to present a comprehensive study of the detectability of ChatGPT-...

Face recognition systems have been targeted by recent physical adversarial machine learning attacks, which attach or project visible patterns on adversaries' faces to trick backend FR models. While these attacks have demonstrated effectiveness in the ...

Automotive companion apps are mobile apps designed to remotely connect with cars to provide features such as diagnostics, logging, navigation, and safety alerts. Specifically, onboard diagnostics (OBD) based mobile applications directly ...

Generative AI models can produce high-quality images based on text prompts. The generated images often appear indistinguishable from images generated by conventional optical photography devices or created by human artists (i.e., real images). ...

Detecting and segmenting polyps is crucial for expediting the diagnosis of colon cancer. This is a challenging task due to the large variations of polyps in color, texture, and lighting conditions, along with subtle differences between the polyp and its ...

With the continuous development of intelligent and networked processes in the automotive industry, consumers' understanding of cars has gradually changed from a means of transportation to a third space connecting home and office. Due to the scattered ...

Ethics is an important criterion for security research. This work presents the current status and trends that security researchers have taken to address ethical concerns in their studies from a data perspective. In particular, we created a dataset of 3,...

This paper concerns with the preassigned-time projective synchronization issue for delayed fully quaternion-valued discontinuous neural networks involving parameter uncertainties through the non-separation method. Above all, based on the existing ...

A well-organized SmartApps dataset provides a valuable resource for researchers to evaluate their work on smart home automation systems. The IoTBench dataset created by Celik et al. 1 is a significant contribution to the IoT research community [1]. ...

In this paper, we propose a Siamese graph learning (SGL) approach to alleviate aging dataset bias. While numerous semi-supervised algorithms have been successfully applied to classification tasks, most of them assume that both the labeled and unlabeled ...

The wide adoption of deep neural networks (DNNs) in real-world applications raises increasing security concerns. Neural Trojans embedded in pre-trained neural networks are a harmful attack against the DNN model supply chain. They generate false outputs ...

The critical challenge of single image inpainting stems from accurate semantic inference via limited information while maintaining image quality. Typical methods for semantic image inpainting train an encoder-decoder network by learning a one-to-one ...

Typical methods for blind image super-resolution (SR) focus on dealing with unknown degradations by directly estimating them or learning the degradation representations in a latent space. A potential limitation of these methods is that they assume the ...

In this paper, the problem of the preassigned-time synchronization is studied for a class of delayed fuzzy cellular neural networks with discontinuous activations via preassigned-time control. Above all, with the help of the existing classical ...

The growth of IoT apps poses increasing concerns about sensitive data leaks. While privacy policies are required to describe how IoT apps use private user data (i.e., data practice), problems such as missing, inaccurate, and inconsistent policies ...

With the growing popularity of artificial intelligence (AI) and machine learning (ML), a wide spectrum of attacks against deep learning (DL) models have been proposed in the literature. Both the evasion attacks and the poisoning attacks attempt to ...

Deep learning-based auto-driving systems are vulnerable to adversarial examples attacks which may result in wrong decision making and accidents. An adversarial example can fool the well trained neural networks by adding barely imperceptible perturbations ...

Fuzzing is one of the most effective approaches to finding software flaws. However, applying it to microcontroller firmware incurs many challenges. For example, rehosting-based solutions cannot accurately model peripheral behaviors and thus cannot be ...

In the evasion attacks against deep neural networks (DNN), the attacker generates adversarial instances that are visually indistinguishable from benign samples and sends them to the target DNN to trigger misclassifications. In this paper, we propose a ...

To detect fraudulent TLS server certificates and improve the accountability of certification authorities (CAs), certificate transparency (CT) is proposed to record certificates in publicly-visible logs, from which the monitors fetch all certificates and ...