Publications (16)
Hide and Seek: on the Stealthiness of Attacks against Deep Learning Systems
Zeyan Liu, Fengjun Li, Jingqiang Lin +2
With the growing popularity of artificial intelligence and machine learning, a wide spectrum of attacks against deep learning models have been proposed in the literature. Both the…
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution
Fengjun Li, Xin Feng, Fanglin Chen +2
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…
Dual State-space Fidelity Blade (D-STAB): A Novel Stealthy Cyber-physical Attack Paradigm
Jiajun Shen, Hao Tu, Fengjun Li +3
This paper presents a novel cyber-physical attack paradigm, termed the Dual State-Space Fidelity Blade (D-STAB), which targets the firmware of core cyber-physical components as a n…
Aggregating Global Features into Local Vision Transformer
Krushi Patel, Andres M. Bur, Fengjun Li +1
Local Transformer-based classification models have recently achieved promising results with relatively low computational costs. However, the effect of aggregating spatial global in…
Multi-Layer Dense Attention Decoder for Polyp Segmentation
Krushi Patel, Fengjun Li, Guanghui Wang
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 lig…
From Library Portability to Para-rehosting: Natively Executing Microcontroller Software on Commodity Hardware
Wenqiang Li, Le Guan, Jingqiang Lin +2
Finding bugs in microcontroller (MCU) firmware is challenging, even for device manufacturers who own the source code. The MCU runs different instruction sets than x86 and exposes a…
Generative Memory-Guided Semantic Reasoning Model for Image Inpainting
Xin Feng, Wenjie Pei, Fengjun Li +3
Most existing methods for image inpainting focus on learning the intra-image priors from the known regions of the current input image to infer the content of the corrupted regions…
Cyber-Physical Systems Security -- A Survey
Abdulmalik Humayed, Jingqiang Lin, Fengjun Li +1
With the exponential growth of cyber-physical systems (CPS), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for…
Model-free Resilient Controller Design based on Incentive Feedback Stackelberg Game and Q-learning
Jiajun Shen, Fengjun Li, Morteza Hashemi +1
In the swift evolution of Cyber-Physical Systems (CPSs) within intelligent environments, especially in the industrial domain shaped by Industry 4.0, the surge in development brings…
The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking
Yuying Li, Zeyan Liu, Junyi Zhao +4
Generative AI models can produce high-quality images based on text prompts. The generated images often appear indistinguishable from images generated by conventional optical photog…
PhantomSeal: Proactive Deepfakes Defense with Identity/Context Protection and Forensic Tracing
Liangqin Ren, Zeyan Liu, Ye Wang +3
Deepfakes, especially face-swapping attacks, pose significant challenges to authenticity, security, and ethics across science, engineering, and society. While most existing detecti…
UPPRESSO: Untraceable and Unlinkable Privacy-PREserving Single Sign-On Services
Chengqian Guo, Jingqiang Lin, Quanwei Cai +6
Single sign-on (SSO) allows a user to maintain only the credential for an identity provider (IdP) to log into multiple relying parties (RPs). However, SSO introduces privacy threat…
AFL: Non-intrusive Feedback-driven Fuzzing for Microcontroller Firmware
Wenqiang Li, Jiameng Shi, Fengjun Li +3
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 sol…
Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency
Sohaib Kiani, Sana Awan, Chao Lan +2
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 th…
PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption
Liangqin Ren, Zeyan Liu, Fengjun Li +3
In the past decade, we have witnessed an exponential growth of deep learning models, platforms, and applications. While existing DL applications and Machine Learning as a service (…
On the Detectability of ChatGPT Content: Benchmarking, Methodology, and Evaluation through the Lens of Academic Writing
Zeyan Liu, Zijun Yao, Fengjun Li +1
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…