Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models

Abstract

Transfer learning provides an effective solution for feasibly and fast customize accurate Student models, by transferring the learned knowledge of pre-trained Teacher models over large datasets via fine-tuning. Many pre-trained Teacher models used in transfer learning are publicly available and maintained by public platforms, increasing their vulnerability to backdoor attacks. In this paper, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly-adopted defenses: pruning-based, retraining-based and input pre-processing-based defenses. Specifically, (A) ranking-based selection mechanism to speed up the backdoor trigger generation and perturbation process while defeating pruning-based and/or retraining-based defenses. (B) autoencoder-powered trigger generation is proposed to produce a robust trigger that can defeat the input pre-processing-based defense, while guaranteeing that selected neuron(s) can be significantly activated. (C) defense-aware retraining to generate the manipulated model using reverse-engineered model inputs. We launch effective misclassification attacks on Student models over real-world images, brain Magnetic Resonance Imaging (MRI) data and Electrocardiography (ECG) learning systems. The experiments reveal that our enhanced attack can maintain the 98.4\% and 97.2\% classification accuracy as the genuine model on clean image and time series inputs respectively while improving 27.9\%-100\% and 27.1\%-56.1\% attack success rate on trojaned image and time series inputs respectively in the presence of pruning-based and/or retraining-based defenses.

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Reproductions