Programmable Neural Network Trojan for Pre-Trained Feature Extractor

Abstract

Neural network (NN) trojaning attack is an emerging and important attack model that can broadly damage the system deployed with NN models. Existing studies have explored the outsourced training attack scenario and transfer learning attack scenario in some small datasets for specific domains, with limited numbers of fixed target classes. In this paper, we propose a more powerful trojaning attack method for both outsourced training attack and transfer learning attack, which outperforms existing studies in the capability, generality, and stealthiness. First, The attack is programmable that the malicious misclassification target is not fixed and can be generated on demand even after the victim's deployment. Second, our trojan attack is not limited in a small domain; one trojaned model on a large-scale dataset can affect applications of different domains that reuse its general features. Thirdly, our trojan design is hard to be detected or eliminated even if the victims fine-tune the whole model.

References in corpus (5)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Deep Residual Learning for Image Recognition
• BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
• Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation

Cited by in corpus (3)

• Input-Aware Dynamic Backdoor Attack
• Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review
• Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-Level Backdoor Attacks