Selective Amnesia: On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models

The extensive applications of deep neural network (DNN) and its increasingly complicated architecture and supply chain make the risk of backdoor attacks more realistic than ever. In such an attack, the adversary either poisons the training data of a DNN model or manipulates its training process to stealthily inject a covert backdoor task, alongside the primary task, so as to strategically misclassify inputs carrying a trigger. Defending against such an attack, particularly removing the backdoor effect from an infected model, is known to be hard. For this purpose, prior research either requires a recovered trigger, which is hard to come by, or attempts to fine-tune a model on its primary task, which becomes less effective when the clean data is scarce. In this paper, we present a simple yet surprisingly effective technique to induce "selective amnesia" on a backdoored model. Our approach, called SEAM, has been inspired by the problem of catastrophic forgetting (CF), a long standing issue in continual learning. Our idea is to retrain a given DNN model on randomly labeled clean data, to induce a CF on the model, leading to a sudden forget on both primary and backdoor tasks; then we recover the primary task by retraining the randomized model on correctly labeled clean data. We analyzed SEAM by modeling the unlearning process as continual learning and further approximating a DNN using Neural Tangent Kernel for measuring CF. Our analysis shows that our random-labeling approach actually maximizes the CF on an unknown backdoor in the absence of triggered inputs, and also preserves some feature extraction in the network to enable a fast revival of the primary task. We further evaluated SEAM on both image processing and Natural Language Processing tasks, under both data contamination and training manipulation attacks, over thousands of models either trained on popular image datasets or provided by the TrojAI competition. Our experiments show that SEAM vastly outperforms the state-of-the-art unlearning techniques, achieving a high Fidelity (measuring the gap between the accuracy of the primary task and that of the backdoor) efficiently (e.g., about 30 times faster than training a model from scratch on the MNIST dataset), with only a small amount of clean data (e.g., with a size of just 0.1% of training data for TrojAI models).