How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data
arXiv:2109.01300
Abstract
Since training a large-scale backdoored model from scratch requires a large training dataset, several recent attacks have considered to inject backdoors into a trained clean model without altering model behaviors on the clean data. Previous work finds that backdoors can be injected into a trained clean model with Adversarial Weight Perturbation (AWP). Here AWPs refers to the variations of parameters that are small in backdoor learning. In this work, we observe an interesting phenomenon that the variations of parameters are always AWPs when tuning the trained clean model to inject backdoors. We further provide theoretical analysis to explain this phenomenon. We formulate the behavior of maintaining accuracy on clean data as the consistency of backdoored models, which includes both global consistency and instance-wise consistency. We extensively analyze the effects of AWPs on the consistency of backdoored models. In order to achieve better consistency, we propose a novel anchoring loss to anchor or freeze the model behaviors on the clean data, with a theoretical guarantee. Both the analytical and the empirical results validate the effectiveness of the anchoring loss in improving the consistency, especially the instance-wise consistency.
Accepted by ICLR 2022
References in corpus (6)
- Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
- Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks
- Can Adversarial Weight Perturbations Inject Neural Backdoors?
- Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
- Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models
- One-pixel Signature: Characterizing CNN Models for Backdoor Detection