11 citations · 33 across the 13 of their papers we have counts for
5 papers · 1 filter
Synthesizing Physical Backdoor Datasets: An Automated Framework Leveraging Deep Generative Models
Sze Jue Yang, Chinh D. La, Quang H. Nguyen +4
Backdoor attacks, representing an emerging threat to the integrity of deep neural networks, have garnered significant attention due to their ability to compromise deep learning sys…
Fooling the Textual Fooler via Randomizing Latent Representations
Duy C. Hoang, Quang H. Nguyen, Saurav Manchanda +3
Despite outstanding performance in a variety of NLP tasks, recent studies have revealed that NLP models are vulnerable to adversarial attacks that slightly perturb the input to cau…
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…
Everyone Can Attack: Repurpose Lossy Compression as a Natural Backdoor Attack
Sze Jue Yang, Quang Nguyen, Chee Seng Chan +1
The vulnerabilities to backdoor attacks have recently threatened the trustworthiness of machine learning models in practical applications. Conventional wisdom suggests that not eve…
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions
Thuy Dung Nguyen, Tuan Nguyen, Phi Le Nguyen +3
Federated learning (FL) is a machine learning (ML) approach that allows the use of distributed data without compromising personal privacy. However, the heterogeneous distribution o…