65 citations · 98 across the 12 of their papers we have counts for
13 papers
Robust Watermarking on Gradient Boosting Decision Trees
Jun Woo Chung, Yingjie Lao, Weijie Zhao
Gradient Boosting Decision Trees (GBDTs) are widely used in industry and academia for their high accuracy and efficiency, particularly on structured data. However, watermarking GBD…
UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models
Huawei Lin, Yingjie Lao, Tong Geng +3
Large Language Models (LLMs) are vulnerable to attacks like prompt injection, backdoor attacks, and adversarial attacks, which manipulate prompts or models to generate harmful outp…
Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data
Huawei Lin, Jun Woo Chung, Yingjie Lao +1
Gradient Boosting Decision Tree (GBDT) is one of the most popular machine learning models in various applications. However, in the traditional settings, all data should be simultan…
DMin: Scalable Training Data Influence Estimation for Diffusion Models
Huawei Lin, Yingjie Lao, Weijie Zhao
Identifying the training data samples that most influence a generated image is a critical task in understanding diffusion models (DMs), yet existing influence estimation methods ar…
UltraClean: A Simple Framework to Train Robust Neural Networks against Backdoor Attacks
Bingyin Zhao, Yingjie Lao
Backdoor attacks are emerging threats to deep neural networks, which typically embed malicious behaviors into a victim model by injecting poisoned samples. Adversaries can activate…
Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks
Quang H. Nguyen, Yingjie Lao, Tung Pham +2
Recent works have shown that deep neural networks are vulnerable to adversarial examples that find samples close to the original image but can make the model misclassify. Even with…
Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class
Khoa D. Doan, Yingjie Lao, Ping Li
In recent years, machine learning models have been shown to be vulnerable to backdoor attacks. Under such attacks, an adversary embeds a stealthy backdoor into the trained model su…
NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Language
Faysal Hossain Shezan, Yingjie Lao, Minlong Peng +3
The recent privacy leakage incidences and the more strict policy regulations demand a much higher standard of compliance for companies and mobile apps. However, such obligations al…
Integrity Authentication in Tree Models
Weijie Zhao, Yingjie Lao, Ping Li
Tree models are very widely used in practice of machine learning and data mining. In this paper, we study the problem of model integrity authentication in tree models. In general,…
Defending Backdoor Attacks on Vision Transformer via Patch Processing
Khoa D. Doan, Yingjie Lao, Peng Yang +1
Vision Transformers (ViTs) have a radically different architecture with significantly less inductive bias than Convolutional Neural Networks. Along with the improvement in performa…
Towards Class-Oriented Poisoning Attacks Against Neural Networks
Bingyin Zhao, Yingjie Lao
Poisoning attacks on machine learning systems compromise the model performance by deliberately injecting malicious samples in the training dataset to influence the training process…
Rallying Adversarial Techniques against Deep Learning for Network Security
Joseph Clements, Yuzhe Yang, Ankur Sharma +2
Recent advances in artificial intelligence and the increasing need for powerful defensive measures in the domain of network security, have led to the adoption of deep learning appr…
Hardware Trojan Attacks on Neural Networks
Joseph Clements, Yingjie Lao
With the rising popularity of machine learning and the ever increasing demand for computational power, there is a growing need for hardware optimized implementations of neural netw…