Efficient Backdoor Removal Through Natural Gradient Fine-tuning

Understand

The success of a deep neural network (DNN) heavily relies on the details of the training scheme; e.g., training data, architectures, hyper-parameters, etc.

• Recent backdoor attacks suggest that an adversary can take advantage of such training details and compromise the integrity of a DNN.
• Our studies show that a backdoor model is usually optimized to a bad local minima, i.e.
• sharper minima as compared to a benign model.