Improving the Transferability of Adversarial Attacks on Face Recognition With Beneficial Perturbation Feature Augmentation

Face recognition (FR) models can be easily fooled by adversarial examples, which are crafted by adding imperceptible perturbations on benign face images. The existence of adversarial face examples poses a great threat to the security of society. To build a more sustainable digital nation, in this article, we improve the transferability of adversarial face examples to expose more blind spots of the existing FR models. Though generating hard samples has shown its effectiveness in improving the generalization of models in training tasks, the effectiveness of using this idea to improve the transferability of adversarial face examples remains unexplored. To this end, based on the property of hard samples and the symmetry between training tasks and adversarial attack tasks, we propose the concept of hard models, which have similar effects as hard samples for adversarial attack tasks. Using the concept of hard models, we propose a novel attack method called beneficial perturbation feature augmentation attack (BPFA), which reduces the overfitting of adversarial examples to surrogate FR models by constantly generating new hard models to craft the adversarial examples. Specifically, in the backpropagation, BPFA records the gradients on preselected feature maps and uses the gradient on the input image to craft the adversarial example. In the next forward propagation, BPFA leverages the recorded gradients to add beneficial perturbations on their corresponding feature maps to increase the loss. Extensive experiments demonstrate that BPFA can significantly boost the transferability of adversarial attacks on FR

Adversarial system · Artificial neural network · Facial recognition system · Generalization · Machine learning · Overfitting · Transferability · Adversarial Robustness in Machine Learning · Computer Science · COVID-19 diagnosis using AI · Mathematics · Artificial Intelligence