20 citations · 54 across the 12 of their papers we have counts for
6 papers · 1 filter
On the Difficulty of Defending Contrastive Learning against Backdoor Attacks
Changjiang Li, Ren Pang, Bochuan Cao +4
Recent studies have shown that contrastive learning, like supervised learning, is highly vulnerable to backdoor attacks wherein malicious functions are injected into target models,…
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…
Neural Architectural Backdoors
Ren Pang, Changjiang Li, Zhaohan Xi +2
This paper asks the intriguing question: is it possible to exploit neural architecture search (NAS) as a new attack vector to launch previously improbable attacks? Specifically, we…
An Embarrassingly Simple Backdoor Attack on Self-supervised Learning
Changjiang Li, Ren Pang, Zhaohan Xi +4
As a new paradigm in machine learning, self-supervised learning (SSL) is capable of learning high-quality representations of complex data without relying on labels. In addition to…
Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era
Changjiang Li, Li Wang, Shouling Ji +4
Facial Liveness Verification (FLV) is widely used for identity authentication in many security-sensitive domains and offered as Platform-as-a-Service (PaaS) by leading cloud vendor…
Towards Robust Reasoning over Knowledge Graphs
Zhaohan Xi, Ren Pang, Changjiang Li +4
Answering complex logical queries over large-scale knowledge graphs (KGs) represents an important artificial intelligence task, entailing a range of applications. Recently, knowled…