Hao Yu (National University of Defense Technology), Chuan Ma (Chongqing University), Xinhang Wan (National University of Defense Technology), Jun Wang (National University of Defense Technology), Tao Xiang (Chongqing University), Meng Shen (Beijing Institute of Technology, Beijing, China), Xinwang Liu (National University of Defense Technology)

Graph Neural Networks (GNNs) are vulnerable to backdoor attacks, where triggers inserted into original graphs cause adversary-determined predictions. Backdoor attacks on GNNs, typically focusing on node classification tasks, are categorized by dirty- and clean-label attacks and pose challenges due to the interconnected nature of normal and poisoned nodes. Current defenses are indeed circumvented by sophisticated triggers and often rely on strong assumptions borrowed from other domains (e.g., rapid loss drops on poisoned images). They lead to high attack risks, failing to effectively protect against both dirty- and clean-label attacks simultaneously. To tackle these challenges, we propose DShield, a comprehensive defense framework with a discrepancy learning mechanism to defend against various graph backdoor attacks. Specifically, we reveal two vital facts during the attacking process: *semantic drift* where dirty-label attacks modify the semantic information of poisoned nodes, and *attribute over-emphasis* where clean-label attacks exaggerate specific attributes to enforce adversary-determined predictions. Motivated by those, DShield employs a self-supervised learning framework to construct a model without relying on manipulated label information. Subsequently, it utilizes both the self-supervised and backdoored models to analyze discrepancies in semantic information and attribute importance, effectively filtering out poisoned nodes. Finally, DShield trains normal models using the preserved nodes, thereby minimizing the impact of poisoned nodes. Compared with 6 state-of-the-art defenses under 21 backdoor attacks, we conduct evaluations on 7 datasets with 2 victim models to demonstrate that DShield effectively mitigates backdoor threats with minimal degradation in performance on normal nodes. For instance, on the Cora dataset, DShield reduces the attack success rate to 1.33% from 54.47% achieved by the second-best defense Prune while maintaining an 82.15% performance on normal nodes. The source code is available at https://github.com/csyuhao/DShield.

View More Papers

Vulnerability, Where Art Thou? An Investigation of Vulnerability Management...

Daniel Klischies (Ruhr University Bochum), Philipp Mackensen (Ruhr University Bochum), Veelasha Moonsamy (Ruhr University Bochum)

Read More

Delay-allowed Differentially Private Data Stream Release

Xiaochen Li (University of Virginia), Zhan Qin (Zhejiang University), Kui Ren (Zhejiang University), Chen Gong (University of Virginia), Shuya Feng (University of Connecticut), Yuan Hong (University of Connecticut), Tianhao Wang (University of Virginia)

Read More

Repurposing Neural Networks for Efficient Cryptographic Computation

Xin Jin (The Ohio State University), Shiqing Ma (University of Massachusetts Amherst), Zhiqiang Lin (The Ohio State University)

Read More

Characterizing the Impact of Audio Deepfakes in the Presence...

Magdalena Pasternak (University of Florida), Kevin Warren (University of Florida), Daniel Olszewski (University of Florida), Susan Nittrouer (University of Florida), Patrick Traynor (University of Florida), Kevin Butler (University of Florida)

Read More