333 citations · 1.3k across the 71 of their papers we have counts for
12 papers · 1 filter
SyzTrust: State-aware Fuzzing on Trusted OS Designed for IoT Devices
Qinying Wang, Boyu Chang, Shouling Ji +8
Trusted Execution Environments (TEEs) embedded in IoT devices provide a deployable solution to secure IoT applications at the hardware level. By design, in TEEs, the Trusted Operat…
Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
Zhaohan Xi, Tianyu Du, Changjiang Li +5
Pre-trained language models (PLMs) have demonstrated remarkable performance as few-shot learners. However, their security risks under such settings are largely unexplored. In this…
On the Security Risks of Knowledge Graph Reasoning
Zhaohan Xi, Tianyu Du, Changjiang Li +6
Knowledge graph reasoning (KGR) -- answering complex logical queries over large knowledge graphs -- represents an important artificial intelligence task, entailing a range of appli…
UVSCAN: Detecting Third-Party Component Usage Violations in IoT Firmware
Binbin Zhao, Shouling Ji, Xuhong Zhang +5
Nowadays, IoT devices integrate a wealth of third-party components (TPCs) in firmware to shorten the development cycle. TPCs usually have strict usage specifications, e.g., checkin…
Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization
Tong Ye, Lingfei Wu, Tengfei Ma +5
Automatically generating human-readable text describing the functionality of a program is the intent of source code summarization. Although neural language models achieve significa…
RNN-Guard: Certified Robustness Against Multi-frame Attacks for Recurrent Neural Networks
Yunruo Zhang, Tianyu Du, Shouling Ji +2
It is well-known that recurrent neural networks (RNNs), although widely used, are vulnerable to adversarial attacks including one-frame attacks and multi-frame attacks. Though a fe…
Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection
Chuer Yu, Xuhong Zhang, Yuxuan Duan +5
Despite the fact that DeepFake forgery detection algorithms have achieved impressive performance on known manipulations, they often face disastrous performance degradation when gen…
Watch Out for the Confusing Faces: Detecting Face Swapping with the Probability Distribution of Face Identification Models
Yuxuan Duan, Xuhong Zhang, Chuer Yu +3
Recently, face swapping has been developing rapidly and achieved a surprising reality, raising concerns about fake content. As a countermeasure, various detection approaches have b…
Edge Deep Learning Model Protection via Neuron Authorization
Jinyin Chen, Haibin Zheng, Tao Liu +4
With the development of deep learning processors and accelerators, deep learning models have been widely deployed on edge devices as part of the Internet of Things. Edge device mod…
MINER: A Hybrid Data-Driven Approach for REST API Fuzzing
Chenyang Lyu, Jiacheng Xu, Shouling Ji +6
In recent years, REST API fuzzing has emerged to explore errors on a cloud service. Its performance highly depends on the sequence construction and request generation. However, exi…
FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases
Chong Fu, Xuhong Zhang, Shouling Ji +4
Trojan attack on deep neural networks, also known as backdoor attack, is a typical threat to artificial intelligence. A trojaned neural network behaves normally with clean inputs.…
TextDefense: Adversarial Text Detection based on Word Importance Entropy
Lujia Shen, Xuhong Zhang, Shouling Ji +4
Currently, natural language processing (NLP) models are wildly used in various scenarios. However, NLP models, like all deep models, are vulnerable to adversarially generated text.…