LoneNeuron: A Highly-Effective Feature-Domain Neural Trojan Using Invisible and Polymorphic Watermarks
Zeyan Liu, Fengjun Li, Zhu Li, Bo Luo
Abstract
The wide adoption of deep neural networks (DNNs) in real-world applications raises increasing security concerns. Neural Trojans embedded in pre-trained neural networks are a harmful attack against the DNN model supply chain. They generate false outputs when certain stealthy triggers appear in the inputs. While data-poisoning attacks have been well studied in the literature, code-poisoning and model-poisoning backdoors only start to attract attention until recently. We present a novel model-poisoning neural Trojan, namely LoneNeuron, which responds to feature-domain patterns that transform into invisible, sample-specific, and polymorphic pixel-domain watermarks. With high attack specificity, LoneNeuron achieves a 100% attack success rate, while not affecting the main task performance. With LoneNeuron's unique watermark polymorphism property, the same feature-domain trigger is resolved to multiple watermarks in the pixel domain, which further improves watermark randomness, stealthiness, and resistance against Trojan detection. Extensive experiments show that LoneNeuron could escape state-of-the-art Trojan detectors. LoneNeuron~is also the first effective backdoor attack against vision transformers (ViTs).